2022/10/10 16:07:51 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] CUDA available: True numpy_random_seed: 824879492 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) PyTorch: 1.12.0+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.2.1 - Built with CuDNN 8.3.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.0+cu113 OpenCV: 4.6.0 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/10/10 16:07:52 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict(type='ViPNAS_MobileNetV3'), head=dict( type='ViPNASHead', in_channels=160, out_channels=17, deconv_out_channels=(160, 160, 160), deconv_num_groups=(160, 160, 160), loss=dict(type='KeypointMSELoss', use_target_weight=True), decoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict( type='RandomBBoxTransform', rotate_factor=60, scale_factor=(0.75, 1.25)), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict( type='RandomBBoxTransform', rotate_factor=60, scale_factor=(0.75, 1.25)), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = 'work_dirs/202210010/vipnas_mbv3/' 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer wrapper constructor" registry tree. As a workaround, the current "optimizer wrapper constructor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer" registry tree. As a workaround, the current "optimizer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:23 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:26 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:28 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:28 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:28 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/10 16:08:28 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([16, 3, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.conv1.bn.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.conv1.bn.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.depthwise_conv.conv.weight - torch.Size([16, 2, 3, 3]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.depthwise_conv.bn.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.depthwise_conv.bn.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.se.conv1.conv.weight - torch.Size([4, 16, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.se.conv1.conv.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.se.conv2.conv.weight - torch.Size([16, 4, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.se.conv2.conv.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.linear_conv.conv.weight - torch.Size([16, 16, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.linear_conv.bn.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.linear_conv.bn.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.expand_conv.conv.weight - torch.Size([120, 16, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.expand_conv.conv.weight - torch.Size([120, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.expand_conv.conv.weight - torch.Size([120, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.expand_conv.conv.weight - torch.Size([120, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.expand_conv.conv.weight - torch.Size([160, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer6.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer6.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer6.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.expand_conv.conv.weight - torch.Size([160, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer7.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer7.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer7.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.expand_conv.conv.weight - torch.Size([160, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer8.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer8.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer8.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.expand_conv.conv.weight - torch.Size([160, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer9.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer9.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer9.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.expand_conv.conv.weight - torch.Size([400, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.expand_conv.conv.weight - torch.Size([400, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.expand_conv.conv.weight - torch.Size([400, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.expand_conv.conv.weight - torch.Size([400, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.expand_conv.conv.weight - torch.Size([560, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.expand_conv.conv.weight - torch.Size([560, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.expand_conv.conv.weight - torch.Size([560, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.expand_conv.conv.weight - torch.Size([560, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.expand_conv.conv.weight - torch.Size([960, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.expand_conv.conv.weight - torch.Size([960, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.expand_conv.conv.weight - torch.Size([960, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.expand_conv.conv.weight - torch.Size([960, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.0.weight - torch.Size([160, 1, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.1.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.1.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.3.weight - torch.Size([160, 1, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.4.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.4.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.6.weight - torch.Size([160, 1, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.7.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.7.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.final_layer.weight - torch.Size([17, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 head.final_layer.bias - torch.Size([17]): NormalInit: mean=0, std=0.001, bias=0 2022/10/10 16:08:28 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3 by HardDiskBackend. 2022/10/10 16:09:19 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 17:28:01 time: 1.022797 data_time: 0.203843 memory: 6293 loss_kpt: 0.002317 acc_pose: 0.138350 loss: 0.002317 2022/10/10 16:09:48 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 13:41:50 time: 0.582615 data_time: 0.156623 memory: 6293 loss_kpt: 0.002105 acc_pose: 0.214012 loss: 0.002105 2022/10/10 16:10:17 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 12:25:10 time: 0.579847 data_time: 0.176168 memory: 6293 loss_kpt: 0.001960 acc_pose: 0.278761 loss: 0.001960 2022/10/10 16:10:44 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 11:31:32 time: 0.520922 data_time: 0.091169 memory: 6293 loss_kpt: 0.001861 acc_pose: 0.341002 loss: 0.001861 2022/10/10 16:11:06 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 10:45:46 time: 0.455289 data_time: 0.078461 memory: 6293 loss_kpt: 0.001786 acc_pose: 0.355122 loss: 0.001786 2022/10/10 16:11:26 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:11:47 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 8:53:59 time: 0.430643 data_time: 0.087081 memory: 6293 loss_kpt: 0.001681 acc_pose: 0.376672 loss: 0.001681 2022/10/10 16:12:06 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 8:33:11 time: 0.366508 data_time: 0.077242 memory: 6293 loss_kpt: 0.001656 acc_pose: 0.420418 loss: 0.001656 2022/10/10 16:12:24 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 8:17:07 time: 0.367500 data_time: 0.074837 memory: 6293 loss_kpt: 0.001603 acc_pose: 0.511378 loss: 0.001603 2022/10/10 16:12:44 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 8:08:26 time: 0.408161 data_time: 0.074463 memory: 6293 loss_kpt: 0.001558 acc_pose: 0.453021 loss: 0.001558 2022/10/10 16:13:07 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 8:04:58 time: 0.447367 data_time: 0.105036 memory: 6293 loss_kpt: 0.001531 acc_pose: 0.497139 loss: 0.001531 2022/10/10 16:13:24 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:13:44 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 7:25:05 time: 0.396879 data_time: 0.087154 memory: 6293 loss_kpt: 0.001450 acc_pose: 0.553437 loss: 0.001450 2022/10/10 16:14:03 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 7:20:01 time: 0.374863 data_time: 0.076965 memory: 6293 loss_kpt: 0.001429 acc_pose: 0.485082 loss: 0.001429 2022/10/10 16:14:21 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 7:15:45 time: 0.377252 data_time: 0.074236 memory: 6293 loss_kpt: 0.001409 acc_pose: 0.526022 loss: 0.001409 2022/10/10 16:14:39 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 7:10:52 time: 0.359801 data_time: 0.074226 memory: 6293 loss_kpt: 0.001385 acc_pose: 0.525846 loss: 0.001385 2022/10/10 16:14:58 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 7:07:24 time: 0.374231 data_time: 0.075618 memory: 6293 loss_kpt: 0.001371 acc_pose: 0.616469 loss: 0.001371 2022/10/10 16:15:14 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:15:33 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 6:44:03 time: 0.368307 data_time: 0.080567 memory: 6293 loss_kpt: 0.001348 acc_pose: 0.584466 loss: 0.001348 2022/10/10 16:15:52 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 6:43:16 time: 0.391191 data_time: 0.076706 memory: 6293 loss_kpt: 0.001320 acc_pose: 0.554907 loss: 0.001320 2022/10/10 16:16:00 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:16:11 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 6:41:25 time: 0.368870 data_time: 0.078479 memory: 6293 loss_kpt: 0.001295 acc_pose: 0.617192 loss: 0.001295 2022/10/10 16:16:29 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 6:39:48 time: 0.370588 data_time: 0.075282 memory: 6293 loss_kpt: 0.001288 acc_pose: 0.614524 loss: 0.001288 2022/10/10 16:16:48 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 6:38:42 time: 0.379593 data_time: 0.083245 memory: 6293 loss_kpt: 0.001294 acc_pose: 0.625524 loss: 0.001294 2022/10/10 16:17:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:17:24 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 6:24:55 time: 0.416400 data_time: 0.089943 memory: 6293 loss_kpt: 0.001255 acc_pose: 0.552937 loss: 0.001255 2022/10/10 16:17:44 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 6:25:01 time: 0.393449 data_time: 0.075952 memory: 6293 loss_kpt: 0.001245 acc_pose: 0.638407 loss: 0.001245 2022/10/10 16:18:04 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 6:25:36 time: 0.407172 data_time: 0.067692 memory: 6293 loss_kpt: 0.001237 acc_pose: 0.560811 loss: 0.001237 2022/10/10 16:18:25 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 6:26:07 time: 0.407147 data_time: 0.092920 memory: 6293 loss_kpt: 0.001230 acc_pose: 0.598768 loss: 0.001230 2022/10/10 16:18:44 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 6:25:57 time: 0.389699 data_time: 0.096344 memory: 6293 loss_kpt: 0.001219 acc_pose: 0.532513 loss: 0.001219 2022/10/10 16:19:00 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:19:19 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 6:14:09 time: 0.376828 data_time: 0.089362 memory: 6293 loss_kpt: 0.001207 acc_pose: 0.563093 loss: 0.001207 2022/10/10 16:19:38 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 6:14:14 time: 0.386891 data_time: 0.065132 memory: 6293 loss_kpt: 0.001189 acc_pose: 0.607481 loss: 0.001189 2022/10/10 16:19:57 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 6:14:07 time: 0.380351 data_time: 0.083816 memory: 6293 loss_kpt: 0.001167 acc_pose: 0.597217 loss: 0.001167 2022/10/10 16:20:16 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 6:14:01 time: 0.381697 data_time: 0.081758 memory: 6293 loss_kpt: 0.001187 acc_pose: 0.601857 loss: 0.001187 2022/10/10 16:20:35 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 6:13:36 time: 0.371343 data_time: 0.075004 memory: 6293 loss_kpt: 0.001158 acc_pose: 0.642121 loss: 0.001158 2022/10/10 16:20:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:21:11 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 6:04:50 time: 0.400063 data_time: 0.108437 memory: 6293 loss_kpt: 0.001157 acc_pose: 0.689550 loss: 0.001157 2022/10/10 16:21:30 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 6:04:37 time: 0.369635 data_time: 0.086283 memory: 6293 loss_kpt: 0.001160 acc_pose: 0.629277 loss: 0.001160 2022/10/10 16:21:48 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 6:04:26 time: 0.371146 data_time: 0.074683 memory: 6293 loss_kpt: 0.001160 acc_pose: 0.595615 loss: 0.001160 2022/10/10 16:22:06 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 6:04:05 time: 0.365397 data_time: 0.075003 memory: 6293 loss_kpt: 0.001144 acc_pose: 0.566040 loss: 0.001144 2022/10/10 16:22:23 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:22:26 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 6:04:38 time: 0.401410 data_time: 0.065797 memory: 6293 loss_kpt: 0.001135 acc_pose: 0.653443 loss: 0.001135 2022/10/10 16:22:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:23:02 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 5:56:52 time: 0.377901 data_time: 0.093501 memory: 6293 loss_kpt: 0.001128 acc_pose: 0.577323 loss: 0.001128 2022/10/10 16:23:21 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 5:57:06 time: 0.383593 data_time: 0.081411 memory: 6293 loss_kpt: 0.001125 acc_pose: 0.665595 loss: 0.001125 2022/10/10 16:23:39 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 5:57:02 time: 0.371149 data_time: 0.074777 memory: 6293 loss_kpt: 0.001112 acc_pose: 0.638252 loss: 0.001112 2022/10/10 16:23:59 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 5:57:23 time: 0.390657 data_time: 0.068762 memory: 6293 loss_kpt: 0.001099 acc_pose: 0.653059 loss: 0.001099 2022/10/10 16:24:17 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 5:57:12 time: 0.367516 data_time: 0.066831 memory: 6293 loss_kpt: 0.001103 acc_pose: 0.630924 loss: 0.001103 2022/10/10 16:24:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:24:55 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:51:16 time: 0.411899 data_time: 0.091337 memory: 6293 loss_kpt: 0.001091 acc_pose: 0.685508 loss: 0.001091 2022/10/10 16:25:16 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:52:13 time: 0.418834 data_time: 0.063755 memory: 6293 loss_kpt: 0.001102 acc_pose: 0.656383 loss: 0.001102 2022/10/10 16:25:34 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:52:06 time: 0.366702 data_time: 0.076676 memory: 6293 loss_kpt: 0.001092 acc_pose: 0.664203 loss: 0.001092 2022/10/10 16:25:54 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:52:40 time: 0.402099 data_time: 0.069479 memory: 6293 loss_kpt: 0.001077 acc_pose: 0.645549 loss: 0.001077 2022/10/10 16:26:15 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:53:18 time: 0.408153 data_time: 0.075020 memory: 6293 loss_kpt: 0.001086 acc_pose: 0.649392 loss: 0.001086 2022/10/10 16:26:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:26:50 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 5:47:40 time: 0.391180 data_time: 0.089622 memory: 6293 loss_kpt: 0.001074 acc_pose: 0.700235 loss: 0.001074 2022/10/10 16:27:10 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 5:48:14 time: 0.402512 data_time: 0.078615 memory: 6293 loss_kpt: 0.001065 acc_pose: 0.674379 loss: 0.001065 2022/10/10 16:27:30 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 5:48:30 time: 0.387897 data_time: 0.071142 memory: 6293 loss_kpt: 0.001079 acc_pose: 0.637553 loss: 0.001079 2022/10/10 16:27:50 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 5:48:59 time: 0.401050 data_time: 0.087688 memory: 6293 loss_kpt: 0.001079 acc_pose: 0.632833 loss: 0.001079 2022/10/10 16:28:10 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 5:49:24 time: 0.398553 data_time: 0.090172 memory: 6293 loss_kpt: 0.001061 acc_pose: 0.637172 loss: 0.001061 2022/10/10 16:28:25 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:28:25 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/10 16:28:35 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:00:59 time: 0.167884 data_time: 0.104758 memory: 6293 2022/10/10 16:28:41 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:37 time: 0.121285 data_time: 0.056563 memory: 533 2022/10/10 16:28:47 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:31 time: 0.121312 data_time: 0.057762 memory: 533 2022/10/10 16:28:53 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:24 time: 0.119779 data_time: 0.055811 memory: 533 2022/10/10 16:28:59 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:16 time: 0.106899 data_time: 0.045304 memory: 533 2022/10/10 16:29:04 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:12 time: 0.112613 data_time: 0.050470 memory: 533 2022/10/10 16:29:10 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:06 time: 0.108129 data_time: 0.046625 memory: 533 2022/10/10 16:29:16 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.123750 data_time: 0.061033 memory: 533 2022/10/10 16:29:50 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 16:30:03 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.531585 coco/AP .5: 0.804899 coco/AP .75: 0.585309 coco/AP (M): 0.510054 coco/AP (L): 0.579165 coco/AR: 0.598630 coco/AR .5: 0.856423 coco/AR .75: 0.654282 coco/AR (M): 0.564764 coco/AR (L): 0.646563 2022/10/10 16:30:04 - mmengine - INFO - The best checkpoint with 0.5316 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/10 16:30:24 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 5:44:29 time: 0.398852 data_time: 0.102453 memory: 6293 loss_kpt: 0.001048 acc_pose: 0.700154 loss: 0.001048 2022/10/10 16:30:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:30:42 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 5:44:18 time: 0.359623 data_time: 0.067824 memory: 6293 loss_kpt: 0.001053 acc_pose: 0.665058 loss: 0.001053 2022/10/10 16:31:01 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 5:44:23 time: 0.377030 data_time: 0.081310 memory: 6293 loss_kpt: 0.001057 acc_pose: 0.658808 loss: 0.001057 2022/10/10 16:31:20 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 5:44:26 time: 0.376027 data_time: 0.070182 memory: 6293 loss_kpt: 0.001043 acc_pose: 0.695188 loss: 0.001043 2022/10/10 16:31:39 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 5:44:31 time: 0.378826 data_time: 0.076461 memory: 6293 loss_kpt: 0.001044 acc_pose: 0.676996 loss: 0.001044 2022/10/10 16:31:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:32:15 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 5:39:59 time: 0.390222 data_time: 0.079719 memory: 6293 loss_kpt: 0.001050 acc_pose: 0.692843 loss: 0.001050 2022/10/10 16:32:34 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 5:40:13 time: 0.385284 data_time: 0.084948 memory: 6293 loss_kpt: 0.001058 acc_pose: 0.697734 loss: 0.001058 2022/10/10 16:32:53 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 5:40:19 time: 0.378714 data_time: 0.075289 memory: 6293 loss_kpt: 0.001044 acc_pose: 0.690332 loss: 0.001044 2022/10/10 16:33:11 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 5:40:05 time: 0.355037 data_time: 0.074552 memory: 6293 loss_kpt: 0.001043 acc_pose: 0.660997 loss: 0.001043 2022/10/10 16:33:29 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 5:39:56 time: 0.361549 data_time: 0.069581 memory: 6293 loss_kpt: 0.001047 acc_pose: 0.687994 loss: 0.001047 2022/10/10 16:33:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:34:05 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 5:36:03 time: 0.407326 data_time: 0.108863 memory: 6293 loss_kpt: 0.001007 acc_pose: 0.664817 loss: 0.001007 2022/10/10 16:34:23 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 5:35:56 time: 0.360557 data_time: 0.070430 memory: 6293 loss_kpt: 0.001009 acc_pose: 0.654454 loss: 0.001009 2022/10/10 16:34:41 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 5:35:43 time: 0.353212 data_time: 0.073426 memory: 6293 loss_kpt: 0.001025 acc_pose: 0.652484 loss: 0.001025 2022/10/10 16:34:58 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 5:35:25 time: 0.348107 data_time: 0.070261 memory: 6293 loss_kpt: 0.001022 acc_pose: 0.720035 loss: 0.001022 2022/10/10 16:35:17 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 5:35:32 time: 0.379865 data_time: 0.075069 memory: 6293 loss_kpt: 0.000994 acc_pose: 0.713355 loss: 0.000994 2022/10/10 16:35:32 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:35:51 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 5:31:44 time: 0.386052 data_time: 0.083104 memory: 6293 loss_kpt: 0.001000 acc_pose: 0.741072 loss: 0.001000 2022/10/10 16:36:09 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 5:31:37 time: 0.358914 data_time: 0.066530 memory: 6293 loss_kpt: 0.001016 acc_pose: 0.681017 loss: 0.001016 2022/10/10 16:36:28 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 5:31:36 time: 0.367622 data_time: 0.071781 memory: 6293 loss_kpt: 0.000991 acc_pose: 0.678941 loss: 0.000991 2022/10/10 16:36:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:36:47 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 5:31:39 time: 0.374355 data_time: 0.063817 memory: 6293 loss_kpt: 0.001016 acc_pose: 0.761507 loss: 0.001016 2022/10/10 16:37:05 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 5:31:37 time: 0.367223 data_time: 0.068943 memory: 6293 loss_kpt: 0.000992 acc_pose: 0.722805 loss: 0.000992 2022/10/10 16:37:20 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:37:38 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 5:27:55 time: 0.369759 data_time: 0.104880 memory: 6293 loss_kpt: 0.000996 acc_pose: 0.737596 loss: 0.000996 2022/10/10 16:37:56 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 5:27:45 time: 0.353447 data_time: 0.068113 memory: 6293 loss_kpt: 0.000986 acc_pose: 0.701822 loss: 0.000986 2022/10/10 16:38:14 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 5:27:46 time: 0.369442 data_time: 0.072581 memory: 6293 loss_kpt: 0.000985 acc_pose: 0.660542 loss: 0.000985 2022/10/10 16:38:34 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 5:28:00 time: 0.390750 data_time: 0.077034 memory: 6293 loss_kpt: 0.000971 acc_pose: 0.706970 loss: 0.000971 2022/10/10 16:38:53 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 5:28:06 time: 0.377982 data_time: 0.072539 memory: 6293 loss_kpt: 0.000977 acc_pose: 0.724314 loss: 0.000977 2022/10/10 16:39:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:39:29 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 5:24:48 time: 0.382709 data_time: 0.092190 memory: 6293 loss_kpt: 0.000988 acc_pose: 0.754894 loss: 0.000988 2022/10/10 16:39:47 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 5:24:47 time: 0.367162 data_time: 0.071687 memory: 6293 loss_kpt: 0.000986 acc_pose: 0.698512 loss: 0.000986 2022/10/10 16:40:06 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 5:24:48 time: 0.369624 data_time: 0.072396 memory: 6293 loss_kpt: 0.000978 acc_pose: 0.690737 loss: 0.000978 2022/10/10 16:40:24 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 5:24:50 time: 0.373483 data_time: 0.073377 memory: 6293 loss_kpt: 0.000974 acc_pose: 0.648063 loss: 0.000974 2022/10/10 16:40:44 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 5:24:58 time: 0.382649 data_time: 0.075094 memory: 6293 loss_kpt: 0.000973 acc_pose: 0.722321 loss: 0.000973 2022/10/10 16:40:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:41:19 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 5:22:00 time: 0.394787 data_time: 0.092172 memory: 6293 loss_kpt: 0.000975 acc_pose: 0.686640 loss: 0.000975 2022/10/10 16:41:37 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 5:21:57 time: 0.363936 data_time: 0.083068 memory: 6293 loss_kpt: 0.000971 acc_pose: 0.735761 loss: 0.000971 2022/10/10 16:41:56 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 5:22:01 time: 0.375583 data_time: 0.079342 memory: 6293 loss_kpt: 0.000955 acc_pose: 0.711528 loss: 0.000955 2022/10/10 16:42:13 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 5:21:50 time: 0.350756 data_time: 0.065203 memory: 6293 loss_kpt: 0.000990 acc_pose: 0.714081 loss: 0.000990 2022/10/10 16:42:31 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 5:21:43 time: 0.358655 data_time: 0.075042 memory: 6293 loss_kpt: 0.000993 acc_pose: 0.759665 loss: 0.000993 2022/10/10 16:42:47 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:42:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:43:07 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 5:18:58 time: 0.397515 data_time: 0.103647 memory: 6293 loss_kpt: 0.000964 acc_pose: 0.685712 loss: 0.000964 2022/10/10 16:43:25 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 5:18:59 time: 0.371736 data_time: 0.067880 memory: 6293 loss_kpt: 0.000961 acc_pose: 0.721518 loss: 0.000961 2022/10/10 16:43:45 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 5:19:12 time: 0.393189 data_time: 0.067346 memory: 6293 loss_kpt: 0.000954 acc_pose: 0.683645 loss: 0.000954 2022/10/10 16:44:04 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 5:19:13 time: 0.372134 data_time: 0.086517 memory: 6293 loss_kpt: 0.000947 acc_pose: 0.723543 loss: 0.000947 2022/10/10 16:44:23 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 5:19:19 time: 0.382140 data_time: 0.082803 memory: 6293 loss_kpt: 0.000954 acc_pose: 0.724028 loss: 0.000954 2022/10/10 16:44:38 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:44:57 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 5:16:31 time: 0.376108 data_time: 0.082734 memory: 6293 loss_kpt: 0.000950 acc_pose: 0.733326 loss: 0.000950 2022/10/10 16:45:17 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 5:16:46 time: 0.398602 data_time: 0.071215 memory: 6293 loss_kpt: 0.000938 acc_pose: 0.784841 loss: 0.000938 2022/10/10 16:45:35 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 5:16:46 time: 0.371032 data_time: 0.095980 memory: 6293 loss_kpt: 0.000950 acc_pose: 0.736702 loss: 0.000950 2022/10/10 16:45:54 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 5:16:49 time: 0.377668 data_time: 0.078115 memory: 6293 loss_kpt: 0.000953 acc_pose: 0.768223 loss: 0.000953 2022/10/10 16:46:13 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 5:16:53 time: 0.380418 data_time: 0.074167 memory: 6293 loss_kpt: 0.000947 acc_pose: 0.638071 loss: 0.000947 2022/10/10 16:46:29 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:46:49 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 5:14:25 time: 0.396667 data_time: 0.084921 memory: 6293 loss_kpt: 0.000932 acc_pose: 0.705529 loss: 0.000932 2022/10/10 16:47:08 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 5:14:32 time: 0.385934 data_time: 0.070454 memory: 6293 loss_kpt: 0.000948 acc_pose: 0.686693 loss: 0.000948 2022/10/10 16:47:27 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 5:14:32 time: 0.371980 data_time: 0.076255 memory: 6293 loss_kpt: 0.000924 acc_pose: 0.775246 loss: 0.000924 2022/10/10 16:47:46 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 5:14:37 time: 0.384187 data_time: 0.074084 memory: 6293 loss_kpt: 0.000949 acc_pose: 0.709002 loss: 0.000949 2022/10/10 16:48:07 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 5:14:56 time: 0.414208 data_time: 0.071463 memory: 6293 loss_kpt: 0.000950 acc_pose: 0.718497 loss: 0.000950 2022/10/10 16:48:22 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:48:22 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/10 16:48:30 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:44 time: 0.123490 data_time: 0.058709 memory: 6293 2022/10/10 16:48:36 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:33 time: 0.108483 data_time: 0.041473 memory: 533 2022/10/10 16:48:42 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:31 time: 0.123641 data_time: 0.061330 memory: 533 2022/10/10 16:48:48 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:26 time: 0.128612 data_time: 0.063911 memory: 533 2022/10/10 16:48:54 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:18 time: 0.119657 data_time: 0.057382 memory: 533 2022/10/10 16:49:00 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:12 time: 0.116793 data_time: 0.053194 memory: 533 2022/10/10 16:49:06 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.115105 data_time: 0.053160 memory: 533 2022/10/10 16:49:12 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.112640 data_time: 0.051167 memory: 533 2022/10/10 16:49:44 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 16:49:56 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.590111 coco/AP .5: 0.837313 coco/AP .75: 0.662993 coco/AP (M): 0.566491 coco/AP (L): 0.644372 coco/AR: 0.655463 coco/AR .5: 0.885390 coco/AR .75: 0.726385 coco/AR (M): 0.618192 coco/AR (L): 0.708250 2022/10/10 16:49:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_10.pth is removed 2022/10/10 16:49:58 - mmengine - INFO - The best checkpoint with 0.5901 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/10 16:50:19 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 5:12:50 time: 0.428899 data_time: 0.108373 memory: 6293 loss_kpt: 0.000938 acc_pose: 0.756379 loss: 0.000938 2022/10/10 16:50:38 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 5:12:56 time: 0.386247 data_time: 0.068586 memory: 6293 loss_kpt: 0.000942 acc_pose: 0.607473 loss: 0.000942 2022/10/10 16:50:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:50:57 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 5:12:59 time: 0.382110 data_time: 0.070941 memory: 6293 loss_kpt: 0.000944 acc_pose: 0.754208 loss: 0.000944 2022/10/10 16:51:17 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 5:13:04 time: 0.386611 data_time: 0.073655 memory: 6293 loss_kpt: 0.000929 acc_pose: 0.692726 loss: 0.000929 2022/10/10 16:51:35 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 5:13:03 time: 0.373302 data_time: 0.068952 memory: 6293 loss_kpt: 0.000921 acc_pose: 0.738626 loss: 0.000921 2022/10/10 16:51:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:52:11 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 5:10:42 time: 0.385683 data_time: 0.087950 memory: 6293 loss_kpt: 0.000939 acc_pose: 0.683979 loss: 0.000939 2022/10/10 16:52:30 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 5:10:47 time: 0.384523 data_time: 0.073174 memory: 6293 loss_kpt: 0.000921 acc_pose: 0.727766 loss: 0.000921 2022/10/10 16:52:49 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 5:10:50 time: 0.384667 data_time: 0.085710 memory: 6293 loss_kpt: 0.000928 acc_pose: 0.742857 loss: 0.000928 2022/10/10 16:53:08 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 5:10:54 time: 0.385435 data_time: 0.083166 memory: 6293 loss_kpt: 0.000929 acc_pose: 0.713328 loss: 0.000929 2022/10/10 16:53:27 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 5:10:49 time: 0.365738 data_time: 0.074562 memory: 6293 loss_kpt: 0.000924 acc_pose: 0.731533 loss: 0.000924 2022/10/10 16:53:42 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:54:01 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 5:08:35 time: 0.386588 data_time: 0.086097 memory: 6293 loss_kpt: 0.000928 acc_pose: 0.737279 loss: 0.000928 2022/10/10 16:54:20 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 5:08:39 time: 0.385046 data_time: 0.087579 memory: 6293 loss_kpt: 0.000929 acc_pose: 0.746025 loss: 0.000929 2022/10/10 16:54:39 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 5:08:37 time: 0.373748 data_time: 0.071237 memory: 6293 loss_kpt: 0.000923 acc_pose: 0.770614 loss: 0.000923 2022/10/10 16:54:58 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 5:08:36 time: 0.374525 data_time: 0.076997 memory: 6293 loss_kpt: 0.000909 acc_pose: 0.750618 loss: 0.000909 2022/10/10 16:55:16 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 5:08:28 time: 0.358427 data_time: 0.071594 memory: 6293 loss_kpt: 0.000938 acc_pose: 0.724024 loss: 0.000938 2022/10/10 16:55:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:55:49 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 5:06:12 time: 0.368380 data_time: 0.087467 memory: 6293 loss_kpt: 0.000912 acc_pose: 0.707691 loss: 0.000912 2022/10/10 16:56:06 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 5:05:58 time: 0.343341 data_time: 0.075352 memory: 6293 loss_kpt: 0.000917 acc_pose: 0.690804 loss: 0.000917 2022/10/10 16:56:24 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 5:05:48 time: 0.352819 data_time: 0.079525 memory: 6293 loss_kpt: 0.000903 acc_pose: 0.737080 loss: 0.000903 2022/10/10 16:56:41 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 5:05:33 time: 0.340810 data_time: 0.074648 memory: 6293 loss_kpt: 0.000912 acc_pose: 0.725486 loss: 0.000912 2022/10/10 16:56:58 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 5:05:21 time: 0.347672 data_time: 0.078930 memory: 6293 loss_kpt: 0.000908 acc_pose: 0.706475 loss: 0.000908 2022/10/10 16:57:03 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:57:15 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 16:57:33 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 5:03:09 time: 0.362549 data_time: 0.075418 memory: 6293 loss_kpt: 0.000902 acc_pose: 0.750498 loss: 0.000902 2022/10/10 16:57:52 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 5:03:06 time: 0.369777 data_time: 0.077864 memory: 6293 loss_kpt: 0.000899 acc_pose: 0.719610 loss: 0.000899 2022/10/10 16:58:10 - 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mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 4:59:03 time: 0.374338 data_time: 0.071154 memory: 6293 loss_kpt: 0.000880 acc_pose: 0.755219 loss: 0.000880 2022/10/10 17:02:11 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 4:59:01 time: 0.376673 data_time: 0.070564 memory: 6293 loss_kpt: 0.000918 acc_pose: 0.770858 loss: 0.000918 2022/10/10 17:02:30 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 4:59:00 time: 0.381741 data_time: 0.076625 memory: 6293 loss_kpt: 0.000886 acc_pose: 0.715758 loss: 0.000886 2022/10/10 17:02:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:03:05 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 4:57:11 time: 0.388630 data_time: 0.086932 memory: 6293 loss_kpt: 0.000891 acc_pose: 0.664153 loss: 0.000891 2022/10/10 17:03:21 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:03:25 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 4:57:17 time: 0.402018 data_time: 0.113600 memory: 6293 loss_kpt: 0.000907 acc_pose: 0.750613 loss: 0.000907 2022/10/10 17:03:45 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 4:57:20 time: 0.391369 data_time: 0.080835 memory: 6293 loss_kpt: 0.000873 acc_pose: 0.785108 loss: 0.000873 2022/10/10 17:04:05 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 4:57:24 time: 0.396967 data_time: 0.103366 memory: 6293 loss_kpt: 0.000899 acc_pose: 0.734228 loss: 0.000899 2022/10/10 17:04:23 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 4:57:20 time: 0.372501 data_time: 0.071409 memory: 6293 loss_kpt: 0.000885 acc_pose: 0.759553 loss: 0.000885 2022/10/10 17:04:39 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:04:58 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 4:55:30 time: 0.377753 data_time: 0.089434 memory: 6293 loss_kpt: 0.000887 acc_pose: 0.733957 loss: 0.000887 2022/10/10 17:05:16 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 4:55:22 time: 0.359056 data_time: 0.072767 memory: 6293 loss_kpt: 0.000888 acc_pose: 0.710907 loss: 0.000888 2022/10/10 17:05:35 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 4:55:15 time: 0.364526 data_time: 0.070175 memory: 6293 loss_kpt: 0.000886 acc_pose: 0.792087 loss: 0.000886 2022/10/10 17:05:54 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 4:55:19 time: 0.397770 data_time: 0.071864 memory: 6293 loss_kpt: 0.000896 acc_pose: 0.775013 loss: 0.000896 2022/10/10 17:06:13 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 4:55:16 time: 0.375423 data_time: 0.078128 memory: 6293 loss_kpt: 0.000888 acc_pose: 0.770644 loss: 0.000888 2022/10/10 17:06:30 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:06:49 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 4:53:30 time: 0.381301 data_time: 0.099377 memory: 6293 loss_kpt: 0.000876 acc_pose: 0.701020 loss: 0.000876 2022/10/10 17:07:07 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 4:53:26 time: 0.372124 data_time: 0.082819 memory: 6293 loss_kpt: 0.000883 acc_pose: 0.739506 loss: 0.000883 2022/10/10 17:07:25 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 4:53:15 time: 0.352160 data_time: 0.065914 memory: 6293 loss_kpt: 0.000872 acc_pose: 0.751887 loss: 0.000872 2022/10/10 17:07:43 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 4:53:06 time: 0.357921 data_time: 0.074372 memory: 6293 loss_kpt: 0.000881 acc_pose: 0.741956 loss: 0.000881 2022/10/10 17:08:02 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 4:53:01 time: 0.371093 data_time: 0.074337 memory: 6293 loss_kpt: 0.000880 acc_pose: 0.774666 loss: 0.000880 2022/10/10 17:08:19 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:08:19 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/10 17:08:27 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:43 time: 0.123137 data_time: 0.059059 memory: 6293 2022/10/10 17:08:34 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:37 time: 0.121003 data_time: 0.059161 memory: 533 2022/10/10 17:08:40 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:33 time: 0.131147 data_time: 0.063214 memory: 533 2022/10/10 17:08:46 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:22 time: 0.110896 data_time: 0.047734 memory: 533 2022/10/10 17:08:51 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:16 time: 0.107242 data_time: 0.045339 memory: 533 2022/10/10 17:08:56 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:11 time: 0.109163 data_time: 0.046280 memory: 533 2022/10/10 17:09:02 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:06 time: 0.109571 data_time: 0.046587 memory: 533 2022/10/10 17:09:08 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.123833 data_time: 0.062030 memory: 533 2022/10/10 17:09:39 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 17:09:51 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.617485 coco/AP .5: 0.851289 coco/AP .75: 0.698522 coco/AP (M): 0.590660 coco/AP (L): 0.674612 coco/AR: 0.680793 coco/AR .5: 0.897355 coco/AR .75: 0.755982 coco/AR (M): 0.643431 coco/AR (L): 0.733519 2022/10/10 17:09:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_20.pth is removed 2022/10/10 17:09:53 - mmengine - INFO - The best checkpoint with 0.6175 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/10 17:10:13 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 4:51:25 time: 0.400574 data_time: 0.099561 memory: 6293 loss_kpt: 0.000876 acc_pose: 0.770807 loss: 0.000876 2022/10/10 17:10:31 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 4:51:16 time: 0.356885 data_time: 0.076134 memory: 6293 loss_kpt: 0.000894 acc_pose: 0.806884 loss: 0.000894 2022/10/10 17:10:51 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 4:51:19 time: 0.400148 data_time: 0.064125 memory: 6293 loss_kpt: 0.000862 acc_pose: 0.714715 loss: 0.000862 2022/10/10 17:11:09 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 4:51:08 time: 0.349729 data_time: 0.069713 memory: 6293 loss_kpt: 0.000889 acc_pose: 0.753773 loss: 0.000889 2022/10/10 17:11:12 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:11:27 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 4:51:01 time: 0.366814 data_time: 0.069595 memory: 6293 loss_kpt: 0.000867 acc_pose: 0.728691 loss: 0.000867 2022/10/10 17:11:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:12:05 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:49:32 time: 0.417649 data_time: 0.091167 memory: 6293 loss_kpt: 0.000832 acc_pose: 0.733718 loss: 0.000832 2022/10/10 17:12:23 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:49:24 time: 0.361726 data_time: 0.068657 memory: 6293 loss_kpt: 0.000880 acc_pose: 0.733681 loss: 0.000880 2022/10/10 17:12:43 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:49:26 time: 0.394885 data_time: 0.072661 memory: 6293 loss_kpt: 0.000881 acc_pose: 0.766673 loss: 0.000881 2022/10/10 17:13:02 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:49:23 time: 0.382105 data_time: 0.068570 memory: 6293 loss_kpt: 0.000878 acc_pose: 0.752598 loss: 0.000878 2022/10/10 17:13:20 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:49:18 time: 0.373928 data_time: 0.076356 memory: 6293 loss_kpt: 0.000881 acc_pose: 0.799865 loss: 0.000881 2022/10/10 17:13:37 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:13:57 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:47:47 time: 0.400650 data_time: 0.098633 memory: 6293 loss_kpt: 0.000869 acc_pose: 0.759599 loss: 0.000869 2022/10/10 17:14:15 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:47:39 time: 0.363340 data_time: 0.074509 memory: 6293 loss_kpt: 0.000857 acc_pose: 0.730836 loss: 0.000857 2022/10/10 17:14:37 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:47:51 time: 0.436759 data_time: 0.073201 memory: 6293 loss_kpt: 0.000857 acc_pose: 0.730202 loss: 0.000857 2022/10/10 17:14:58 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:47:59 time: 0.422592 data_time: 0.075005 memory: 6293 loss_kpt: 0.000862 acc_pose: 0.733111 loss: 0.000862 2022/10/10 17:15:17 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:47:56 time: 0.380914 data_time: 0.079138 memory: 6293 loss_kpt: 0.000857 acc_pose: 0.741933 loss: 0.000857 2022/10/10 17:15:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:15:51 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:46:23 time: 0.386269 data_time: 0.093694 memory: 6293 loss_kpt: 0.000862 acc_pose: 0.757745 loss: 0.000862 2022/10/10 17:16:10 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:46:20 time: 0.385571 data_time: 0.075616 memory: 6293 loss_kpt: 0.000857 acc_pose: 0.782333 loss: 0.000857 2022/10/10 17:16:30 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:46:19 time: 0.391287 data_time: 0.076496 memory: 6293 loss_kpt: 0.000851 acc_pose: 0.713910 loss: 0.000851 2022/10/10 17:16:49 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:46:18 time: 0.391415 data_time: 0.076050 memory: 6293 loss_kpt: 0.000868 acc_pose: 0.734993 loss: 0.000868 2022/10/10 17:17:09 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:46:17 time: 0.392364 data_time: 0.076640 memory: 6293 loss_kpt: 0.000872 acc_pose: 0.767978 loss: 0.000872 2022/10/10 17:17:24 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:17:40 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:17:44 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:44:52 time: 0.407037 data_time: 0.086756 memory: 6293 loss_kpt: 0.000850 acc_pose: 0.736839 loss: 0.000850 2022/10/10 17:18:03 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:44:47 time: 0.379790 data_time: 0.073945 memory: 6293 loss_kpt: 0.000854 acc_pose: 0.709384 loss: 0.000854 2022/10/10 17:18:23 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:44:44 time: 0.382686 data_time: 0.071994 memory: 6293 loss_kpt: 0.000874 acc_pose: 0.696566 loss: 0.000874 2022/10/10 17:18:41 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:44:39 time: 0.377877 data_time: 0.068484 memory: 6293 loss_kpt: 0.000846 acc_pose: 0.779531 loss: 0.000846 2022/10/10 17:19:00 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:44:30 time: 0.364677 data_time: 0.065170 memory: 6293 loss_kpt: 0.000868 acc_pose: 0.790371 loss: 0.000868 2022/10/10 17:19:16 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:19:35 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:43:01 time: 0.385329 data_time: 0.097242 memory: 6293 loss_kpt: 0.000861 acc_pose: 0.782165 loss: 0.000861 2022/10/10 17:19:54 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:42:56 time: 0.377940 data_time: 0.076622 memory: 6293 loss_kpt: 0.000850 acc_pose: 0.733730 loss: 0.000850 2022/10/10 17:20:12 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:42:46 time: 0.356675 data_time: 0.083695 memory: 6293 loss_kpt: 0.000852 acc_pose: 0.793380 loss: 0.000852 2022/10/10 17:20:32 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:42:48 time: 0.410302 data_time: 0.092245 memory: 6293 loss_kpt: 0.000851 acc_pose: 0.778072 loss: 0.000851 2022/10/10 17:20:53 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:42:50 time: 0.406814 data_time: 0.080105 memory: 6293 loss_kpt: 0.000865 acc_pose: 0.717731 loss: 0.000865 2022/10/10 17:21:08 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:21:27 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:41:21 time: 0.376727 data_time: 0.083379 memory: 6293 loss_kpt: 0.000862 acc_pose: 0.790943 loss: 0.000862 2022/10/10 17:21:46 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:41:15 time: 0.377814 data_time: 0.072292 memory: 6293 loss_kpt: 0.000852 acc_pose: 0.747057 loss: 0.000852 2022/10/10 17:22:05 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:41:14 time: 0.395932 data_time: 0.091775 memory: 6293 loss_kpt: 0.000859 acc_pose: 0.764433 loss: 0.000859 2022/10/10 17:22:25 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:41:09 time: 0.383306 data_time: 0.073880 memory: 6293 loss_kpt: 0.000849 acc_pose: 0.761292 loss: 0.000849 2022/10/10 17:22:44 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:41:05 time: 0.385291 data_time: 0.073997 memory: 6293 loss_kpt: 0.000865 acc_pose: 0.799654 loss: 0.000865 2022/10/10 17:22:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:23:20 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:39:46 time: 0.410593 data_time: 0.080282 memory: 6293 loss_kpt: 0.000834 acc_pose: 0.773633 loss: 0.000834 2022/10/10 17:23:38 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:39:39 time: 0.372196 data_time: 0.074612 memory: 6293 loss_kpt: 0.000845 acc_pose: 0.775523 loss: 0.000845 2022/10/10 17:23:57 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:39:32 time: 0.374228 data_time: 0.072145 memory: 6293 loss_kpt: 0.000856 acc_pose: 0.738979 loss: 0.000856 2022/10/10 17:24:01 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:24:16 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:39:24 time: 0.367972 data_time: 0.069642 memory: 6293 loss_kpt: 0.000847 acc_pose: 0.784253 loss: 0.000847 2022/10/10 17:24:34 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:39:14 time: 0.360682 data_time: 0.075090 memory: 6293 loss_kpt: 0.000843 acc_pose: 0.776159 loss: 0.000843 2022/10/10 17:24:49 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:25:10 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 4:37:57 time: 0.416685 data_time: 0.086490 memory: 6293 loss_kpt: 0.000850 acc_pose: 0.800388 loss: 0.000850 2022/10/10 17:25:30 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:37:57 time: 0.405883 data_time: 0.069362 memory: 6293 loss_kpt: 0.000843 acc_pose: 0.736572 loss: 0.000843 2022/10/10 17:25:49 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:37:49 time: 0.369877 data_time: 0.073254 memory: 6293 loss_kpt: 0.000856 acc_pose: 0.717387 loss: 0.000856 2022/10/10 17:26:08 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 4:37:44 time: 0.381594 data_time: 0.067862 memory: 6293 loss_kpt: 0.000823 acc_pose: 0.726559 loss: 0.000823 2022/10/10 17:26:27 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 4:37:37 time: 0.374509 data_time: 0.071916 memory: 6293 loss_kpt: 0.000814 acc_pose: 0.819654 loss: 0.000814 2022/10/10 17:26:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:27:03 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 4:36:14 time: 0.380013 data_time: 0.086030 memory: 6293 loss_kpt: 0.000849 acc_pose: 0.752554 loss: 0.000849 2022/10/10 17:27:21 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 4:36:07 time: 0.374680 data_time: 0.065891 memory: 6293 loss_kpt: 0.000844 acc_pose: 0.798551 loss: 0.000844 2022/10/10 17:27:40 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 4:35:59 time: 0.374065 data_time: 0.073402 memory: 6293 loss_kpt: 0.000841 acc_pose: 0.793322 loss: 0.000841 2022/10/10 17:28:00 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 4:35:55 time: 0.390097 data_time: 0.084283 memory: 6293 loss_kpt: 0.000831 acc_pose: 0.736613 loss: 0.000831 2022/10/10 17:28:18 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 4:35:45 time: 0.359995 data_time: 0.070453 memory: 6293 loss_kpt: 0.000825 acc_pose: 0.781402 loss: 0.000825 2022/10/10 17:28:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:28:34 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/10 17:28:42 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:43 time: 0.121649 data_time: 0.056763 memory: 6293 2022/10/10 17:28:48 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:36 time: 0.120482 data_time: 0.055169 memory: 533 2022/10/10 17:28:54 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:30 time: 0.117960 data_time: 0.055236 memory: 533 2022/10/10 17:28:59 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:22 time: 0.109322 data_time: 0.047010 memory: 533 2022/10/10 17:29:06 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:21 time: 0.135586 data_time: 0.073169 memory: 533 2022/10/10 17:29:12 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:11 time: 0.107304 data_time: 0.044372 memory: 533 2022/10/10 17:29:18 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.119554 data_time: 0.057283 memory: 533 2022/10/10 17:29:23 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.110381 data_time: 0.049564 memory: 533 2022/10/10 17:29:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 17:30:06 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.635460 coco/AP .5: 0.858476 coco/AP .75: 0.713814 coco/AP (M): 0.607861 coco/AP (L): 0.693274 coco/AR: 0.696946 coco/AR .5: 0.902550 coco/AR .75: 0.768892 coco/AR (M): 0.658645 coco/AR (L): 0.751691 2022/10/10 17:30:06 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_30.pth is removed 2022/10/10 17:30:08 - mmengine - INFO - The best checkpoint with 0.6355 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/10 17:30:27 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 4:34:25 time: 0.384092 data_time: 0.091884 memory: 6293 loss_kpt: 0.000832 acc_pose: 0.799072 loss: 0.000832 2022/10/10 17:30:45 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 4:34:15 time: 0.364663 data_time: 0.066498 memory: 6293 loss_kpt: 0.000824 acc_pose: 0.786718 loss: 0.000824 2022/10/10 17:31:04 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 4:34:05 time: 0.362380 data_time: 0.076698 memory: 6293 loss_kpt: 0.000836 acc_pose: 0.775572 loss: 0.000836 2022/10/10 17:31:23 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 4:34:01 time: 0.391362 data_time: 0.075284 memory: 6293 loss_kpt: 0.000845 acc_pose: 0.754088 loss: 0.000845 2022/10/10 17:31:42 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 4:33:54 time: 0.376297 data_time: 0.080545 memory: 6293 loss_kpt: 0.000850 acc_pose: 0.777968 loss: 0.000850 2022/10/10 17:31:53 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:31:57 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:32:16 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 4:32:32 time: 0.366826 data_time: 0.084363 memory: 6293 loss_kpt: 0.000837 acc_pose: 0.712787 loss: 0.000837 2022/10/10 17:32:34 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 4:32:21 time: 0.358614 data_time: 0.071261 memory: 6293 loss_kpt: 0.000821 acc_pose: 0.834247 loss: 0.000821 2022/10/10 17:32:52 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 4:32:11 time: 0.363594 data_time: 0.071501 memory: 6293 loss_kpt: 0.000838 acc_pose: 0.720090 loss: 0.000838 2022/10/10 17:33:11 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 4:32:04 time: 0.377671 data_time: 0.074636 memory: 6293 loss_kpt: 0.000836 acc_pose: 0.747305 loss: 0.000836 2022/10/10 17:33:30 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 4:31:59 time: 0.386826 data_time: 0.088454 memory: 6293 loss_kpt: 0.000836 acc_pose: 0.733671 loss: 0.000836 2022/10/10 17:33:45 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:34:04 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 4:30:38 time: 0.367188 data_time: 0.087721 memory: 6293 loss_kpt: 0.000821 acc_pose: 0.787191 loss: 0.000821 2022/10/10 17:34:22 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 4:30:28 time: 0.362082 data_time: 0.068544 memory: 6293 loss_kpt: 0.000824 acc_pose: 0.768234 loss: 0.000824 2022/10/10 17:34:41 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 4:30:20 time: 0.374031 data_time: 0.069122 memory: 6293 loss_kpt: 0.000815 acc_pose: 0.766920 loss: 0.000815 2022/10/10 17:35:01 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 4:30:17 time: 0.400039 data_time: 0.086375 memory: 6293 loss_kpt: 0.000822 acc_pose: 0.779500 loss: 0.000822 2022/10/10 17:35:19 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 4:30:08 time: 0.365972 data_time: 0.083187 memory: 6293 loss_kpt: 0.000818 acc_pose: 0.766110 loss: 0.000818 2022/10/10 17:35:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:35:55 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 4:28:53 time: 0.390036 data_time: 0.079676 memory: 6293 loss_kpt: 0.000810 acc_pose: 0.781879 loss: 0.000810 2022/10/10 17:36:15 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 4:28:48 time: 0.387027 data_time: 0.087962 memory: 6293 loss_kpt: 0.000836 acc_pose: 0.784518 loss: 0.000836 2022/10/10 17:36:33 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 4:28:37 time: 0.360137 data_time: 0.074188 memory: 6293 loss_kpt: 0.000847 acc_pose: 0.791677 loss: 0.000847 2022/10/10 17:36:51 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 4:28:29 time: 0.374977 data_time: 0.072979 memory: 6293 loss_kpt: 0.000814 acc_pose: 0.791554 loss: 0.000814 2022/10/10 17:37:09 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 4:28:19 time: 0.362809 data_time: 0.069740 memory: 6293 loss_kpt: 0.000843 acc_pose: 0.755163 loss: 0.000843 2022/10/10 17:37:24 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:37:43 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 4:27:02 time: 0.370953 data_time: 0.095989 memory: 6293 loss_kpt: 0.000822 acc_pose: 0.749466 loss: 0.000822 2022/10/10 17:38:01 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 4:26:50 time: 0.354273 data_time: 0.070266 memory: 6293 loss_kpt: 0.000822 acc_pose: 0.765787 loss: 0.000822 2022/10/10 17:38:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:38:19 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 4:26:40 time: 0.362690 data_time: 0.072731 memory: 6293 loss_kpt: 0.000819 acc_pose: 0.831380 loss: 0.000819 2022/10/10 17:38:37 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 4:26:29 time: 0.359650 data_time: 0.069600 memory: 6293 loss_kpt: 0.000814 acc_pose: 0.843774 loss: 0.000814 2022/10/10 17:38:55 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 4:26:20 time: 0.373030 data_time: 0.075712 memory: 6293 loss_kpt: 0.000820 acc_pose: 0.783405 loss: 0.000820 2022/10/10 17:39:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:39:31 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 4:25:11 time: 0.403243 data_time: 0.088596 memory: 6293 loss_kpt: 0.000823 acc_pose: 0.747810 loss: 0.000823 2022/10/10 17:39:50 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 4:25:06 time: 0.395827 data_time: 0.085337 memory: 6293 loss_kpt: 0.000820 acc_pose: 0.792834 loss: 0.000820 2022/10/10 17:40:09 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 4:24:57 time: 0.368838 data_time: 0.070411 memory: 6293 loss_kpt: 0.000820 acc_pose: 0.787579 loss: 0.000820 2022/10/10 17:40:27 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 4:24:46 time: 0.361895 data_time: 0.077673 memory: 6293 loss_kpt: 0.000821 acc_pose: 0.753134 loss: 0.000821 2022/10/10 17:40:45 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 4:24:36 time: 0.363521 data_time: 0.068664 memory: 6293 loss_kpt: 0.000820 acc_pose: 0.743015 loss: 0.000820 2022/10/10 17:41:00 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:41:18 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 4:23:21 time: 0.367083 data_time: 0.088855 memory: 6293 loss_kpt: 0.000818 acc_pose: 0.768548 loss: 0.000818 2022/10/10 17:41:36 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 4:23:08 time: 0.349615 data_time: 0.072285 memory: 6293 loss_kpt: 0.000824 acc_pose: 0.765437 loss: 0.000824 2022/10/10 17:41:54 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 4:22:58 time: 0.365991 data_time: 0.085486 memory: 6293 loss_kpt: 0.000802 acc_pose: 0.753755 loss: 0.000802 2022/10/10 17:42:12 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 4:22:47 time: 0.360114 data_time: 0.082084 memory: 6293 loss_kpt: 0.000813 acc_pose: 0.775034 loss: 0.000813 2022/10/10 17:42:31 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 4:22:38 time: 0.368878 data_time: 0.071353 memory: 6293 loss_kpt: 0.000811 acc_pose: 0.800760 loss: 0.000811 2022/10/10 17:42:47 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:43:07 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 4:21:32 time: 0.410081 data_time: 0.096730 memory: 6293 loss_kpt: 0.000816 acc_pose: 0.754004 loss: 0.000816 2022/10/10 17:43:25 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 4:21:21 time: 0.363491 data_time: 0.074830 memory: 6293 loss_kpt: 0.000814 acc_pose: 0.767764 loss: 0.000814 2022/10/10 17:43:44 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 4:21:11 time: 0.363990 data_time: 0.077511 memory: 6293 loss_kpt: 0.000807 acc_pose: 0.721140 loss: 0.000807 2022/10/10 17:44:02 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 4:21:02 time: 0.371159 data_time: 0.077090 memory: 6293 loss_kpt: 0.000822 acc_pose: 0.786615 loss: 0.000822 2022/10/10 17:44:12 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:44:19 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 4:20:48 time: 0.344505 data_time: 0.079426 memory: 6293 loss_kpt: 0.000816 acc_pose: 0.799990 loss: 0.000816 2022/10/10 17:44:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:44:55 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 4:19:39 time: 0.385746 data_time: 0.087284 memory: 6293 loss_kpt: 0.000810 acc_pose: 0.774112 loss: 0.000810 2022/10/10 17:45:14 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 4:19:30 time: 0.375020 data_time: 0.097654 memory: 6293 loss_kpt: 0.000807 acc_pose: 0.782507 loss: 0.000807 2022/10/10 17:45:32 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 4:19:21 time: 0.369799 data_time: 0.072577 memory: 6293 loss_kpt: 0.000809 acc_pose: 0.793365 loss: 0.000809 2022/10/10 17:45:50 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 4:19:09 time: 0.354043 data_time: 0.068864 memory: 6293 loss_kpt: 0.000792 acc_pose: 0.813296 loss: 0.000792 2022/10/10 17:46:08 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 4:18:56 time: 0.352750 data_time: 0.068206 memory: 6293 loss_kpt: 0.000803 acc_pose: 0.744350 loss: 0.000803 2022/10/10 17:46:23 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:46:43 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 4:17:53 time: 0.412071 data_time: 0.097015 memory: 6293 loss_kpt: 0.000807 acc_pose: 0.775989 loss: 0.000807 2022/10/10 17:47:03 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 4:17:46 time: 0.386011 data_time: 0.077064 memory: 6293 loss_kpt: 0.000803 acc_pose: 0.796726 loss: 0.000803 2022/10/10 17:47:22 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 4:17:39 time: 0.390484 data_time: 0.065037 memory: 6293 loss_kpt: 0.000799 acc_pose: 0.764615 loss: 0.000799 2022/10/10 17:47:40 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 4:17:29 time: 0.367392 data_time: 0.072382 memory: 6293 loss_kpt: 0.000827 acc_pose: 0.788569 loss: 0.000827 2022/10/10 17:47:59 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 4:17:20 time: 0.372080 data_time: 0.070203 memory: 6293 loss_kpt: 0.000805 acc_pose: 0.692539 loss: 0.000805 2022/10/10 17:48:15 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:48:15 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/10 17:48:25 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:50 time: 0.142602 data_time: 0.079234 memory: 6293 2022/10/10 17:48:31 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:36 time: 0.120506 data_time: 0.057135 memory: 533 2022/10/10 17:48:37 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:32 time: 0.126477 data_time: 0.062911 memory: 533 2022/10/10 17:48:43 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:25 time: 0.121398 data_time: 0.058268 memory: 533 2022/10/10 17:48:50 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:21 time: 0.137165 data_time: 0.072873 memory: 533 2022/10/10 17:48:56 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:12 time: 0.118125 data_time: 0.055927 memory: 533 2022/10/10 17:49:01 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.106322 data_time: 0.044473 memory: 533 2022/10/10 17:49:06 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.101532 data_time: 0.041747 memory: 533 2022/10/10 17:49:37 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 17:49:49 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.641951 coco/AP .5: 0.861472 coco/AP .75: 0.720025 coco/AP (M): 0.611915 coco/AP (L): 0.702101 coco/AR: 0.702960 coco/AR .5: 0.905699 coco/AR .75: 0.774087 coco/AR (M): 0.662852 coco/AR (L): 0.759606 2022/10/10 17:49:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_40.pth is removed 2022/10/10 17:49:50 - mmengine - INFO - The best checkpoint with 0.6420 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/10 17:50:10 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 4:16:16 time: 0.403771 data_time: 0.109747 memory: 6293 loss_kpt: 0.000802 acc_pose: 0.781634 loss: 0.000802 2022/10/10 17:50:30 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 4:16:10 time: 0.394713 data_time: 0.097534 memory: 6293 loss_kpt: 0.000827 acc_pose: 0.736812 loss: 0.000827 2022/10/10 17:50:50 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 4:16:03 time: 0.390287 data_time: 0.068001 memory: 6293 loss_kpt: 0.000831 acc_pose: 0.724974 loss: 0.000831 2022/10/10 17:51:08 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 4:15:52 time: 0.361141 data_time: 0.075598 memory: 6293 loss_kpt: 0.000816 acc_pose: 0.790169 loss: 0.000816 2022/10/10 17:51:26 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 4:15:43 time: 0.375393 data_time: 0.071456 memory: 6293 loss_kpt: 0.000795 acc_pose: 0.751303 loss: 0.000795 2022/10/10 17:51:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:52:02 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 4:14:34 time: 0.369495 data_time: 0.084998 memory: 6293 loss_kpt: 0.000798 acc_pose: 0.783532 loss: 0.000798 2022/10/10 17:52:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:52:22 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 4:14:28 time: 0.394354 data_time: 0.072179 memory: 6293 loss_kpt: 0.000812 acc_pose: 0.750347 loss: 0.000812 2022/10/10 17:52:40 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 4:14:18 time: 0.367532 data_time: 0.070715 memory: 6293 loss_kpt: 0.000801 acc_pose: 0.808045 loss: 0.000801 2022/10/10 17:53:00 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 4:14:13 time: 0.403955 data_time: 0.081627 memory: 6293 loss_kpt: 0.000807 acc_pose: 0.759570 loss: 0.000807 2022/10/10 17:53:19 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 4:14:05 time: 0.382248 data_time: 0.081052 memory: 6293 loss_kpt: 0.000821 acc_pose: 0.757616 loss: 0.000821 2022/10/10 17:53:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:53:56 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 4:13:01 time: 0.396334 data_time: 0.089450 memory: 6293 loss_kpt: 0.000815 acc_pose: 0.776726 loss: 0.000815 2022/10/10 17:54:15 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 4:12:56 time: 0.397983 data_time: 0.073209 memory: 6293 loss_kpt: 0.000798 acc_pose: 0.771098 loss: 0.000798 2022/10/10 17:54:34 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 4:12:44 time: 0.363782 data_time: 0.075063 memory: 6293 loss_kpt: 0.000791 acc_pose: 0.790175 loss: 0.000791 2022/10/10 17:54:54 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 4:12:38 time: 0.398591 data_time: 0.074472 memory: 6293 loss_kpt: 0.000806 acc_pose: 0.804476 loss: 0.000806 2022/10/10 17:55:15 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 4:12:36 time: 0.422012 data_time: 0.073443 memory: 6293 loss_kpt: 0.000803 acc_pose: 0.792226 loss: 0.000803 2022/10/10 17:55:30 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:55:52 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 4:11:38 time: 0.430427 data_time: 0.124639 memory: 6293 loss_kpt: 0.000798 acc_pose: 0.779869 loss: 0.000798 2022/10/10 17:56:11 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 4:11:30 time: 0.381677 data_time: 0.070823 memory: 6293 loss_kpt: 0.000792 acc_pose: 0.747789 loss: 0.000792 2022/10/10 17:56:28 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 4:11:16 time: 0.350014 data_time: 0.075031 memory: 6293 loss_kpt: 0.000801 acc_pose: 0.837515 loss: 0.000801 2022/10/10 17:56:47 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 4:11:07 time: 0.379542 data_time: 0.073768 memory: 6293 loss_kpt: 0.000814 acc_pose: 0.751377 loss: 0.000814 2022/10/10 17:57:06 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 4:10:57 time: 0.374541 data_time: 0.066536 memory: 6293 loss_kpt: 0.000811 acc_pose: 0.744884 loss: 0.000811 2022/10/10 17:57:22 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:57:43 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 4:09:59 time: 0.416755 data_time: 0.091129 memory: 6293 loss_kpt: 0.000790 acc_pose: 0.789399 loss: 0.000790 2022/10/10 17:58:02 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 4:09:49 time: 0.376375 data_time: 0.067697 memory: 6293 loss_kpt: 0.000793 acc_pose: 0.832970 loss: 0.000793 2022/10/10 17:58:20 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 4:09:38 time: 0.367022 data_time: 0.074470 memory: 6293 loss_kpt: 0.000793 acc_pose: 0.771101 loss: 0.000793 2022/10/10 17:58:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:58:39 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 4:09:28 time: 0.375125 data_time: 0.099331 memory: 6293 loss_kpt: 0.000799 acc_pose: 0.790315 loss: 0.000799 2022/10/10 17:58:58 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 4:09:19 time: 0.382247 data_time: 0.076827 memory: 6293 loss_kpt: 0.000807 acc_pose: 0.722475 loss: 0.000807 2022/10/10 17:59:15 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 17:59:34 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 4:08:17 time: 0.384361 data_time: 0.085493 memory: 6293 loss_kpt: 0.000784 acc_pose: 0.764726 loss: 0.000784 2022/10/10 17:59:52 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 4:08:04 time: 0.354431 data_time: 0.078426 memory: 6293 loss_kpt: 0.000789 acc_pose: 0.729427 loss: 0.000789 2022/10/10 18:00:12 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 4:07:59 time: 0.406428 data_time: 0.078156 memory: 6293 loss_kpt: 0.000798 acc_pose: 0.758174 loss: 0.000798 2022/10/10 18:00:30 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 4:07:47 time: 0.360521 data_time: 0.071505 memory: 6293 loss_kpt: 0.000793 acc_pose: 0.785059 loss: 0.000793 2022/10/10 18:00:50 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 4:07:39 time: 0.391499 data_time: 0.077481 memory: 6293 loss_kpt: 0.000805 acc_pose: 0.795070 loss: 0.000805 2022/10/10 18:01:05 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:01:26 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 4:06:42 time: 0.420198 data_time: 0.093716 memory: 6293 loss_kpt: 0.000786 acc_pose: 0.785127 loss: 0.000786 2022/10/10 18:01:46 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 4:06:34 time: 0.392327 data_time: 0.077632 memory: 6293 loss_kpt: 0.000786 acc_pose: 0.770470 loss: 0.000786 2022/10/10 18:02:05 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 4:06:25 time: 0.380668 data_time: 0.072709 memory: 6293 loss_kpt: 0.000790 acc_pose: 0.822617 loss: 0.000790 2022/10/10 18:02:23 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 4:06:14 time: 0.367111 data_time: 0.074210 memory: 6293 loss_kpt: 0.000812 acc_pose: 0.760404 loss: 0.000812 2022/10/10 18:02:42 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 4:06:02 time: 0.363952 data_time: 0.069696 memory: 6293 loss_kpt: 0.000806 acc_pose: 0.791128 loss: 0.000806 2022/10/10 18:02:58 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:03:19 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 4:05:05 time: 0.414324 data_time: 0.089323 memory: 6293 loss_kpt: 0.000786 acc_pose: 0.787771 loss: 0.000786 2022/10/10 18:03:39 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 4:04:57 time: 0.395070 data_time: 0.070873 memory: 6293 loss_kpt: 0.000775 acc_pose: 0.780625 loss: 0.000775 2022/10/10 18:03:59 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 4:04:50 time: 0.400761 data_time: 0.075484 memory: 6293 loss_kpt: 0.000792 acc_pose: 0.770029 loss: 0.000792 2022/10/10 18:04:17 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 4:04:38 time: 0.359441 data_time: 0.069354 memory: 6293 loss_kpt: 0.000790 acc_pose: 0.824290 loss: 0.000790 2022/10/10 18:04:36 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 4:04:30 time: 0.389072 data_time: 0.076500 memory: 6293 loss_kpt: 0.000792 acc_pose: 0.731457 loss: 0.000792 2022/10/10 18:04:52 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:04:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:05:11 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 4:03:30 time: 0.386885 data_time: 0.099751 memory: 6293 loss_kpt: 0.000787 acc_pose: 0.763821 loss: 0.000787 2022/10/10 18:05:30 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 4:03:20 time: 0.379423 data_time: 0.072370 memory: 6293 loss_kpt: 0.000793 acc_pose: 0.794724 loss: 0.000793 2022/10/10 18:05:50 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 4:03:12 time: 0.395557 data_time: 0.074779 memory: 6293 loss_kpt: 0.000800 acc_pose: 0.754946 loss: 0.000800 2022/10/10 18:06:09 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 4:03:01 time: 0.370794 data_time: 0.075259 memory: 6293 loss_kpt: 0.000780 acc_pose: 0.768531 loss: 0.000780 2022/10/10 18:06:29 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 4:02:55 time: 0.409487 data_time: 0.074347 memory: 6293 loss_kpt: 0.000798 acc_pose: 0.834489 loss: 0.000798 2022/10/10 18:06:45 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:07:04 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 4:01:56 time: 0.386764 data_time: 0.082100 memory: 6293 loss_kpt: 0.000782 acc_pose: 0.790842 loss: 0.000782 2022/10/10 18:07:23 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 4:01:46 time: 0.379006 data_time: 0.077476 memory: 6293 loss_kpt: 0.000793 acc_pose: 0.792270 loss: 0.000793 2022/10/10 18:07:42 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 4:01:33 time: 0.362285 data_time: 0.075794 memory: 6293 loss_kpt: 0.000792 acc_pose: 0.812468 loss: 0.000792 2022/10/10 18:07:59 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 4:01:20 time: 0.354234 data_time: 0.080371 memory: 6293 loss_kpt: 0.000803 acc_pose: 0.720526 loss: 0.000803 2022/10/10 18:08:17 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 4:01:08 time: 0.364009 data_time: 0.081366 memory: 6293 loss_kpt: 0.000779 acc_pose: 0.813062 loss: 0.000779 2022/10/10 18:08:33 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:08:33 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/10 18:08:41 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:40 time: 0.114080 data_time: 0.048698 memory: 6293 2022/10/10 18:08:46 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:33 time: 0.108447 data_time: 0.046325 memory: 533 2022/10/10 18:08:52 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:29 time: 0.113143 data_time: 0.050471 memory: 533 2022/10/10 18:08:58 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:24 time: 0.116915 data_time: 0.052973 memory: 533 2022/10/10 18:09:04 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:18 time: 0.120756 data_time: 0.056857 memory: 533 2022/10/10 18:09:10 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:12 time: 0.115087 data_time: 0.053450 memory: 533 2022/10/10 18:09:16 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.120266 data_time: 0.055336 memory: 533 2022/10/10 18:09:21 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.102130 data_time: 0.040767 memory: 533 2022/10/10 18:09:53 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 18:10:05 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.655337 coco/AP .5: 0.871072 coco/AP .75: 0.733072 coco/AP (M): 0.625578 coco/AP (L): 0.714746 coco/AR: 0.715806 coco/AR .5: 0.912154 coco/AR .75: 0.786996 coco/AR (M): 0.675717 coco/AR (L): 0.773021 2022/10/10 18:10:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_50.pth is removed 2022/10/10 18:10:06 - mmengine - INFO - The best checkpoint with 0.6553 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/10 18:10:25 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 4:00:08 time: 0.373799 data_time: 0.088654 memory: 6293 loss_kpt: 0.000789 acc_pose: 0.793450 loss: 0.000789 2022/10/10 18:10:45 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 4:00:01 time: 0.404063 data_time: 0.075120 memory: 6293 loss_kpt: 0.000764 acc_pose: 0.799320 loss: 0.000764 2022/10/10 18:11:05 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 3:59:52 time: 0.390603 data_time: 0.075731 memory: 6293 loss_kpt: 0.000804 acc_pose: 0.758461 loss: 0.000804 2022/10/10 18:11:24 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 3:59:43 time: 0.386778 data_time: 0.067230 memory: 6293 loss_kpt: 0.000790 acc_pose: 0.809065 loss: 0.000790 2022/10/10 18:11:45 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 3:59:38 time: 0.424327 data_time: 0.083334 memory: 6293 loss_kpt: 0.000787 acc_pose: 0.848256 loss: 0.000787 2022/10/10 18:12:01 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:12:20 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 3:58:41 time: 0.391059 data_time: 0.083099 memory: 6293 loss_kpt: 0.000783 acc_pose: 0.810299 loss: 0.000783 2022/10/10 18:12:41 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 3:58:34 time: 0.406973 data_time: 0.083862 memory: 6293 loss_kpt: 0.000788 acc_pose: 0.791003 loss: 0.000788 2022/10/10 18:12:50 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:12:59 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 3:58:23 time: 0.373316 data_time: 0.071459 memory: 6293 loss_kpt: 0.000790 acc_pose: 0.800189 loss: 0.000790 2022/10/10 18:13:17 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 3:58:10 time: 0.358194 data_time: 0.075968 memory: 6293 loss_kpt: 0.000785 acc_pose: 0.758919 loss: 0.000785 2022/10/10 18:13:36 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 3:57:58 time: 0.365709 data_time: 0.069785 memory: 6293 loss_kpt: 0.000787 acc_pose: 0.791534 loss: 0.000787 2022/10/10 18:13:52 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:14:12 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 3:57:02 time: 0.399588 data_time: 0.089612 memory: 6293 loss_kpt: 0.000791 acc_pose: 0.831204 loss: 0.000791 2022/10/10 18:14:31 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 3:56:52 time: 0.386088 data_time: 0.073378 memory: 6293 loss_kpt: 0.000780 acc_pose: 0.760440 loss: 0.000780 2022/10/10 18:14:50 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 3:56:41 time: 0.371700 data_time: 0.075713 memory: 6293 loss_kpt: 0.000775 acc_pose: 0.771309 loss: 0.000775 2022/10/10 18:15:08 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 3:56:30 time: 0.370475 data_time: 0.079061 memory: 6293 loss_kpt: 0.000796 acc_pose: 0.795187 loss: 0.000796 2022/10/10 18:15:27 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 3:56:18 time: 0.366765 data_time: 0.073490 memory: 6293 loss_kpt: 0.000786 acc_pose: 0.778234 loss: 0.000786 2022/10/10 18:15:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:16:02 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 3:55:22 time: 0.393912 data_time: 0.080849 memory: 6293 loss_kpt: 0.000770 acc_pose: 0.792388 loss: 0.000770 2022/10/10 18:16:21 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 3:55:11 time: 0.377012 data_time: 0.077618 memory: 6293 loss_kpt: 0.000781 acc_pose: 0.813987 loss: 0.000781 2022/10/10 18:16:40 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 3:54:59 time: 0.369220 data_time: 0.082596 memory: 6293 loss_kpt: 0.000783 acc_pose: 0.799910 loss: 0.000783 2022/10/10 18:16:57 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 3:54:45 time: 0.343639 data_time: 0.073259 memory: 6293 loss_kpt: 0.000775 acc_pose: 0.817591 loss: 0.000775 2022/10/10 18:17:16 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 3:54:34 time: 0.377905 data_time: 0.073363 memory: 6293 loss_kpt: 0.000796 acc_pose: 0.799915 loss: 0.000796 2022/10/10 18:17:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:17:51 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 3:53:39 time: 0.402662 data_time: 0.102147 memory: 6293 loss_kpt: 0.000771 acc_pose: 0.762242 loss: 0.000771 2022/10/10 18:18:10 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 3:53:28 time: 0.374273 data_time: 0.075544 memory: 6293 loss_kpt: 0.000764 acc_pose: 0.790062 loss: 0.000764 2022/10/10 18:18:29 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 3:53:17 time: 0.373436 data_time: 0.069680 memory: 6293 loss_kpt: 0.000781 acc_pose: 0.828100 loss: 0.000781 2022/10/10 18:18:48 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 3:53:07 time: 0.382874 data_time: 0.069250 memory: 6293 loss_kpt: 0.000775 acc_pose: 0.778371 loss: 0.000775 2022/10/10 18:19:05 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:19:05 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 3:52:52 time: 0.346424 data_time: 0.071000 memory: 6293 loss_kpt: 0.000779 acc_pose: 0.771587 loss: 0.000779 2022/10/10 18:19:21 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:19:41 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 3:51:57 time: 0.388957 data_time: 0.091009 memory: 6293 loss_kpt: 0.000761 acc_pose: 0.812155 loss: 0.000761 2022/10/10 18:19:59 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 3:51:45 time: 0.370620 data_time: 0.071625 memory: 6293 loss_kpt: 0.000779 acc_pose: 0.740284 loss: 0.000779 2022/10/10 18:20:19 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 3:51:37 time: 0.403916 data_time: 0.067675 memory: 6293 loss_kpt: 0.000776 acc_pose: 0.784695 loss: 0.000776 2022/10/10 18:20:38 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 3:51:24 time: 0.360569 data_time: 0.074065 memory: 6293 loss_kpt: 0.000769 acc_pose: 0.755767 loss: 0.000769 2022/10/10 18:20:57 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 3:51:14 time: 0.386987 data_time: 0.073119 memory: 6293 loss_kpt: 0.000796 acc_pose: 0.772551 loss: 0.000796 2022/10/10 18:21:12 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:21:31 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 3:50:18 time: 0.373098 data_time: 0.090055 memory: 6293 loss_kpt: 0.000771 acc_pose: 0.770781 loss: 0.000771 2022/10/10 18:21:48 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 3:50:04 time: 0.346480 data_time: 0.069109 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.800164 loss: 0.000757 2022/10/10 18:22:05 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 3:49:49 time: 0.343104 data_time: 0.071436 memory: 6293 loss_kpt: 0.000797 acc_pose: 0.810296 loss: 0.000797 2022/10/10 18:22:24 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:49:38 time: 0.382909 data_time: 0.067918 memory: 6293 loss_kpt: 0.000778 acc_pose: 0.780753 loss: 0.000778 2022/10/10 18:22:43 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:49:26 time: 0.369576 data_time: 0.068601 memory: 6293 loss_kpt: 0.000777 acc_pose: 0.834708 loss: 0.000777 2022/10/10 18:22:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:23:18 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:48:31 time: 0.378727 data_time: 0.080334 memory: 6293 loss_kpt: 0.000764 acc_pose: 0.798207 loss: 0.000764 2022/10/10 18:23:35 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:48:18 time: 0.357190 data_time: 0.074158 memory: 6293 loss_kpt: 0.000784 acc_pose: 0.781647 loss: 0.000784 2022/10/10 18:23:54 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:48:06 time: 0.373090 data_time: 0.074276 memory: 6293 loss_kpt: 0.000769 acc_pose: 0.747119 loss: 0.000769 2022/10/10 18:24:12 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:47:52 time: 0.348346 data_time: 0.077613 memory: 6293 loss_kpt: 0.000778 acc_pose: 0.693326 loss: 0.000778 2022/10/10 18:24:30 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:47:40 time: 0.367352 data_time: 0.068868 memory: 6293 loss_kpt: 0.000784 acc_pose: 0.767298 loss: 0.000784 2022/10/10 18:24:45 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:25:05 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:46:46 time: 0.387972 data_time: 0.077323 memory: 6293 loss_kpt: 0.000762 acc_pose: 0.763372 loss: 0.000762 2022/10/10 18:25:14 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:25:23 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:46:35 time: 0.379482 data_time: 0.072595 memory: 6293 loss_kpt: 0.000770 acc_pose: 0.807177 loss: 0.000770 2022/10/10 18:25:42 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:46:23 time: 0.365035 data_time: 0.069819 memory: 6293 loss_kpt: 0.000766 acc_pose: 0.816150 loss: 0.000766 2022/10/10 18:26:01 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:46:12 time: 0.379602 data_time: 0.076240 memory: 6293 loss_kpt: 0.000781 acc_pose: 0.787407 loss: 0.000781 2022/10/10 18:26:20 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:46:01 time: 0.385320 data_time: 0.070250 memory: 6293 loss_kpt: 0.000776 acc_pose: 0.803124 loss: 0.000776 2022/10/10 18:26:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:26:56 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:45:10 time: 0.409672 data_time: 0.106598 memory: 6293 loss_kpt: 0.000769 acc_pose: 0.785182 loss: 0.000769 2022/10/10 18:27:15 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:44:59 time: 0.374349 data_time: 0.071880 memory: 6293 loss_kpt: 0.000765 acc_pose: 0.818017 loss: 0.000765 2022/10/10 18:27:33 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:44:45 time: 0.349776 data_time: 0.075863 memory: 6293 loss_kpt: 0.000765 acc_pose: 0.744906 loss: 0.000765 2022/10/10 18:27:51 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:44:32 time: 0.366092 data_time: 0.078865 memory: 6293 loss_kpt: 0.000767 acc_pose: 0.810968 loss: 0.000767 2022/10/10 18:28:10 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:44:21 time: 0.379848 data_time: 0.082774 memory: 6293 loss_kpt: 0.000763 acc_pose: 0.794834 loss: 0.000763 2022/10/10 18:28:26 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:28:26 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/10 18:28:34 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:41 time: 0.117448 data_time: 0.054737 memory: 6293 2022/10/10 18:28:40 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:35 time: 0.114423 data_time: 0.049690 memory: 533 2022/10/10 18:28:46 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:31 time: 0.123833 data_time: 0.059048 memory: 533 2022/10/10 18:28:51 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:22 time: 0.107947 data_time: 0.045942 memory: 533 2022/10/10 18:28:57 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:17 time: 0.113956 data_time: 0.051585 memory: 533 2022/10/10 18:29:03 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:13 time: 0.126441 data_time: 0.065223 memory: 533 2022/10/10 18:29:09 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.111821 data_time: 0.048678 memory: 533 2022/10/10 18:29:14 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.103024 data_time: 0.042101 memory: 533 2022/10/10 18:29:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 18:29:57 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.663167 coco/AP .5: 0.873475 coco/AP .75: 0.740186 coco/AP (M): 0.634044 coco/AP (L): 0.723432 coco/AR: 0.723174 coco/AR .5: 0.916719 coco/AR .75: 0.792821 coco/AR (M): 0.683720 coco/AR (L): 0.779487 2022/10/10 18:29:57 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_60.pth is removed 2022/10/10 18:29:58 - mmengine - INFO - The best checkpoint with 0.6632 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/10 18:30:20 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:43:32 time: 0.428076 data_time: 0.099475 memory: 6293 loss_kpt: 0.000779 acc_pose: 0.803185 loss: 0.000779 2022/10/10 18:30:42 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:43:28 time: 0.446575 data_time: 0.106626 memory: 6293 loss_kpt: 0.000779 acc_pose: 0.808373 loss: 0.000779 2022/10/10 18:31:04 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:43:23 time: 0.441259 data_time: 0.070374 memory: 6293 loss_kpt: 0.000767 acc_pose: 0.819145 loss: 0.000767 2022/10/10 18:31:25 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:43:15 time: 0.420730 data_time: 0.087635 memory: 6293 loss_kpt: 0.000769 acc_pose: 0.792889 loss: 0.000769 2022/10/10 18:31:45 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:43:05 time: 0.387266 data_time: 0.075378 memory: 6293 loss_kpt: 0.000788 acc_pose: 0.794521 loss: 0.000788 2022/10/10 18:32:03 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:32:27 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:42:21 time: 0.470747 data_time: 0.103189 memory: 6293 loss_kpt: 0.000753 acc_pose: 0.783627 loss: 0.000753 2022/10/10 18:32:49 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:42:16 time: 0.447641 data_time: 0.071753 memory: 6293 loss_kpt: 0.000766 acc_pose: 0.788230 loss: 0.000766 2022/10/10 18:33:10 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:42:08 time: 0.420605 data_time: 0.074281 memory: 6293 loss_kpt: 0.000772 acc_pose: 0.813805 loss: 0.000772 2022/10/10 18:33:30 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:33:31 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:42:01 time: 0.421940 data_time: 0.071150 memory: 6293 loss_kpt: 0.000772 acc_pose: 0.784002 loss: 0.000772 2022/10/10 18:33:52 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:41:52 time: 0.405183 data_time: 0.079735 memory: 6293 loss_kpt: 0.000771 acc_pose: 0.803825 loss: 0.000771 2022/10/10 18:34:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:34:31 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:41:03 time: 0.420535 data_time: 0.103220 memory: 6293 loss_kpt: 0.000768 acc_pose: 0.840700 loss: 0.000768 2022/10/10 18:34:48 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:40:47 time: 0.336535 data_time: 0.071701 memory: 6293 loss_kpt: 0.000771 acc_pose: 0.788301 loss: 0.000771 2022/10/10 18:35:08 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:40:38 time: 0.397353 data_time: 0.084417 memory: 6293 loss_kpt: 0.000759 acc_pose: 0.789420 loss: 0.000759 2022/10/10 18:35:30 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:40:32 time: 0.442369 data_time: 0.085413 memory: 6293 loss_kpt: 0.000780 acc_pose: 0.792676 loss: 0.000780 2022/10/10 18:35:51 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:40:24 time: 0.420023 data_time: 0.070763 memory: 6293 loss_kpt: 0.000755 acc_pose: 0.808495 loss: 0.000755 2022/10/10 18:36:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:36:31 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:39:35 time: 0.412373 data_time: 0.093495 memory: 6293 loss_kpt: 0.000766 acc_pose: 0.776551 loss: 0.000766 2022/10/10 18:36:53 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:39:29 time: 0.443690 data_time: 0.074128 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.812594 loss: 0.000757 2022/10/10 18:37:14 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:39:22 time: 0.430167 data_time: 0.067576 memory: 6293 loss_kpt: 0.000784 acc_pose: 0.798708 loss: 0.000784 2022/10/10 18:37:33 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:39:10 time: 0.373617 data_time: 0.070974 memory: 6293 loss_kpt: 0.000771 acc_pose: 0.818140 loss: 0.000771 2022/10/10 18:37:53 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:39:00 time: 0.406240 data_time: 0.065996 memory: 6293 loss_kpt: 0.000774 acc_pose: 0.776609 loss: 0.000774 2022/10/10 18:38:12 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:38:33 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:38:13 time: 0.431205 data_time: 0.086501 memory: 6293 loss_kpt: 0.000778 acc_pose: 0.777704 loss: 0.000778 2022/10/10 18:38:53 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:38:03 time: 0.402351 data_time: 0.065934 memory: 6293 loss_kpt: 0.000787 acc_pose: 0.706479 loss: 0.000787 2022/10/10 18:39:14 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:37:55 time: 0.418991 data_time: 0.080012 memory: 6293 loss_kpt: 0.000768 acc_pose: 0.817186 loss: 0.000768 2022/10/10 18:39:34 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:37:45 time: 0.402350 data_time: 0.072346 memory: 6293 loss_kpt: 0.000766 acc_pose: 0.788493 loss: 0.000766 2022/10/10 18:39:55 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:37:37 time: 0.418259 data_time: 0.070951 memory: 6293 loss_kpt: 0.000764 acc_pose: 0.851870 loss: 0.000764 2022/10/10 18:40:14 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:40:27 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:40:37 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:36:51 time: 0.450570 data_time: 0.101397 memory: 6293 loss_kpt: 0.000765 acc_pose: 0.789904 loss: 0.000765 2022/10/10 18:40:57 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:36:40 time: 0.391603 data_time: 0.066330 memory: 6293 loss_kpt: 0.000750 acc_pose: 0.804560 loss: 0.000750 2022/10/10 18:41:16 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:36:29 time: 0.390813 data_time: 0.084116 memory: 6293 loss_kpt: 0.000776 acc_pose: 0.775093 loss: 0.000776 2022/10/10 18:41:34 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:36:16 time: 0.368550 data_time: 0.073886 memory: 6293 loss_kpt: 0.000773 acc_pose: 0.804982 loss: 0.000773 2022/10/10 18:41:52 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:36:02 time: 0.348644 data_time: 0.074566 memory: 6293 loss_kpt: 0.000762 acc_pose: 0.788448 loss: 0.000762 2022/10/10 18:42:07 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:42:26 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:35:11 time: 0.382228 data_time: 0.081425 memory: 6293 loss_kpt: 0.000754 acc_pose: 0.834207 loss: 0.000754 2022/10/10 18:42:44 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:34:57 time: 0.363928 data_time: 0.074223 memory: 6293 loss_kpt: 0.000750 acc_pose: 0.818769 loss: 0.000750 2022/10/10 18:43:02 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:34:43 time: 0.359307 data_time: 0.071440 memory: 6293 loss_kpt: 0.000762 acc_pose: 0.828410 loss: 0.000762 2022/10/10 18:43:22 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:34:32 time: 0.389184 data_time: 0.069300 memory: 6293 loss_kpt: 0.000747 acc_pose: 0.810930 loss: 0.000747 2022/10/10 18:43:39 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:34:17 time: 0.343877 data_time: 0.072058 memory: 6293 loss_kpt: 0.000749 acc_pose: 0.741174 loss: 0.000749 2022/10/10 18:43:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:44:14 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:33:26 time: 0.374846 data_time: 0.082441 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.813523 loss: 0.000757 2022/10/10 18:44:35 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:33:16 time: 0.404270 data_time: 0.067589 memory: 6293 loss_kpt: 0.000767 acc_pose: 0.815196 loss: 0.000767 2022/10/10 18:44:54 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:33:05 time: 0.395290 data_time: 0.073108 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.829374 loss: 0.000757 2022/10/10 18:45:16 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:32:57 time: 0.436550 data_time: 0.116786 memory: 6293 loss_kpt: 0.000743 acc_pose: 0.805712 loss: 0.000743 2022/10/10 18:45:38 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:32:50 time: 0.437152 data_time: 0.073672 memory: 6293 loss_kpt: 0.000756 acc_pose: 0.790832 loss: 0.000756 2022/10/10 18:45:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:46:19 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:32:07 time: 0.467630 data_time: 0.090840 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.824905 loss: 0.000757 2022/10/10 18:46:41 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:31:58 time: 0.420802 data_time: 0.101185 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.818430 loss: 0.000757 2022/10/10 18:47:02 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:47:03 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:31:52 time: 0.447894 data_time: 0.089809 memory: 6293 loss_kpt: 0.000744 acc_pose: 0.802858 loss: 0.000744 2022/10/10 18:47:26 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:31:46 time: 0.454190 data_time: 0.063267 memory: 6293 loss_kpt: 0.000752 acc_pose: 0.796677 loss: 0.000752 2022/10/10 18:47:47 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:31:38 time: 0.435064 data_time: 0.077199 memory: 6293 loss_kpt: 0.000760 acc_pose: 0.805808 loss: 0.000760 2022/10/10 18:48:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:48:26 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:30:52 time: 0.430553 data_time: 0.114307 memory: 6293 loss_kpt: 0.000756 acc_pose: 0.790006 loss: 0.000756 2022/10/10 18:48:47 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:30:44 time: 0.436258 data_time: 0.099774 memory: 6293 loss_kpt: 0.000768 acc_pose: 0.783278 loss: 0.000768 2022/10/10 18:49:07 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:30:32 time: 0.387754 data_time: 0.078900 memory: 6293 loss_kpt: 0.000760 acc_pose: 0.806224 loss: 0.000760 2022/10/10 18:49:31 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:30:28 time: 0.477165 data_time: 0.120070 memory: 6293 loss_kpt: 0.000765 acc_pose: 0.751168 loss: 0.000765 2022/10/10 18:49:50 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:30:17 time: 0.398019 data_time: 0.086462 memory: 6293 loss_kpt: 0.000747 acc_pose: 0.754222 loss: 0.000747 2022/10/10 18:50:08 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:50:08 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/10 18:50:18 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:54 time: 0.152234 data_time: 0.090116 memory: 6293 2022/10/10 18:50:23 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:33 time: 0.108187 data_time: 0.045602 memory: 533 2022/10/10 18:50:29 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:28 time: 0.110577 data_time: 0.047760 memory: 533 2022/10/10 18:50:34 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:23 time: 0.114380 data_time: 0.052557 memory: 533 2022/10/10 18:50:41 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:19 time: 0.126305 data_time: 0.063663 memory: 533 2022/10/10 18:50:47 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:13 time: 0.124535 data_time: 0.062281 memory: 533 2022/10/10 18:50:52 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:06 time: 0.111991 data_time: 0.049442 memory: 533 2022/10/10 18:50:59 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.123986 data_time: 0.060849 memory: 533 2022/10/10 18:51:31 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 18:51:43 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.666691 coco/AP .5: 0.873950 coco/AP .75: 0.749007 coco/AP (M): 0.636474 coco/AP (L): 0.728702 coco/AR: 0.726275 coco/AR .5: 0.917034 coco/AR .75: 0.800535 coco/AR (M): 0.685605 coco/AR (L): 0.784244 2022/10/10 18:51:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_70.pth is removed 2022/10/10 18:51:45 - mmengine - INFO - The best checkpoint with 0.6667 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/10 18:52:05 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:29:29 time: 0.409002 data_time: 0.087226 memory: 6293 loss_kpt: 0.000759 acc_pose: 0.787201 loss: 0.000759 2022/10/10 18:52:27 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:29:21 time: 0.430664 data_time: 0.070824 memory: 6293 loss_kpt: 0.000759 acc_pose: 0.833515 loss: 0.000759 2022/10/10 18:52:47 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:29:10 time: 0.405667 data_time: 0.094072 memory: 6293 loss_kpt: 0.000756 acc_pose: 0.798319 loss: 0.000756 2022/10/10 18:53:07 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:29:00 time: 0.403382 data_time: 0.069358 memory: 6293 loss_kpt: 0.000764 acc_pose: 0.793920 loss: 0.000764 2022/10/10 18:53:27 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:28:49 time: 0.405552 data_time: 0.072773 memory: 6293 loss_kpt: 0.000765 acc_pose: 0.787641 loss: 0.000765 2022/10/10 18:53:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:54:09 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:28:06 time: 0.461137 data_time: 0.087166 memory: 6293 loss_kpt: 0.000760 acc_pose: 0.811869 loss: 0.000760 2022/10/10 18:54:29 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:27:56 time: 0.410053 data_time: 0.088508 memory: 6293 loss_kpt: 0.000753 acc_pose: 0.776630 loss: 0.000753 2022/10/10 18:54:52 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:27:48 time: 0.443783 data_time: 0.078733 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.805504 loss: 0.000757 2022/10/10 18:55:11 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:27:36 time: 0.392796 data_time: 0.066745 memory: 6293 loss_kpt: 0.000747 acc_pose: 0.804082 loss: 0.000747 2022/10/10 18:55:32 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:27:26 time: 0.416129 data_time: 0.073314 memory: 6293 loss_kpt: 0.000766 acc_pose: 0.746182 loss: 0.000766 2022/10/10 18:55:39 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:55:49 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 18:56:12 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:26:43 time: 0.452593 data_time: 0.091188 memory: 6293 loss_kpt: 0.000758 acc_pose: 0.816162 loss: 0.000758 2022/10/10 18:56:35 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:26:36 time: 0.457690 data_time: 0.072578 memory: 6293 loss_kpt: 0.000754 acc_pose: 0.795261 loss: 0.000754 2022/10/10 18:56:57 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:26:27 time: 0.430551 data_time: 0.072030 memory: 6293 loss_kpt: 0.000742 acc_pose: 0.797462 loss: 0.000742 2022/10/10 18:57:18 - 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mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 3:24:51 time: 0.426926 data_time: 0.104196 memory: 6293 loss_kpt: 0.000751 acc_pose: 0.763343 loss: 0.000751 2022/10/10 18:59:43 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 3:24:42 time: 0.434235 data_time: 0.093615 memory: 6293 loss_kpt: 0.000747 acc_pose: 0.769024 loss: 0.000747 2022/10/10 19:00:01 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:00:23 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 3:23:58 time: 0.436859 data_time: 0.076639 memory: 6293 loss_kpt: 0.000736 acc_pose: 0.812826 loss: 0.000736 2022/10/10 19:00:44 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 3:23:48 time: 0.420305 data_time: 0.064324 memory: 6293 loss_kpt: 0.000724 acc_pose: 0.844266 loss: 0.000724 2022/10/10 19:01:06 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 3:23:39 time: 0.431883 data_time: 0.091819 memory: 6293 loss_kpt: 0.000753 acc_pose: 0.779252 loss: 0.000753 2022/10/10 19:01:27 - 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mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 3:22:19 time: 0.460252 data_time: 0.072325 memory: 6293 loss_kpt: 0.000749 acc_pose: 0.853756 loss: 0.000749 2022/10/10 19:03:33 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 3:22:09 time: 0.416250 data_time: 0.075995 memory: 6293 loss_kpt: 0.000755 acc_pose: 0.762122 loss: 0.000755 2022/10/10 19:03:55 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 3:21:59 time: 0.436368 data_time: 0.073685 memory: 6293 loss_kpt: 0.000757 acc_pose: 0.767730 loss: 0.000757 2022/10/10 19:04:14 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:04:37 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 3:21:17 time: 0.455727 data_time: 0.097664 memory: 6293 loss_kpt: 0.000746 acc_pose: 0.861529 loss: 0.000746 2022/10/10 19:04:59 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 3:21:08 time: 0.437513 data_time: 0.070159 memory: 6293 loss_kpt: 0.000742 acc_pose: 0.828138 loss: 0.000742 2022/10/10 19:05:20 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 3:20:57 time: 0.419525 data_time: 0.074946 memory: 6293 loss_kpt: 0.000746 acc_pose: 0.819248 loss: 0.000746 2022/10/10 19:05:41 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 3:20:48 time: 0.436526 data_time: 0.071068 memory: 6293 loss_kpt: 0.000733 acc_pose: 0.770397 loss: 0.000733 2022/10/10 19:06:01 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 3:20:36 time: 0.398433 data_time: 0.072353 memory: 6293 loss_kpt: 0.000754 acc_pose: 0.799335 loss: 0.000754 2022/10/10 19:06:19 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:06:41 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 3:19:51 time: 0.427877 data_time: 0.084280 memory: 6293 loss_kpt: 0.000745 acc_pose: 0.812746 loss: 0.000745 2022/10/10 19:07:02 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 3:19:41 time: 0.432761 data_time: 0.075931 memory: 6293 loss_kpt: 0.000745 acc_pose: 0.771982 loss: 0.000745 2022/10/10 19:07:22 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 3:19:29 time: 0.391707 data_time: 0.082396 memory: 6293 loss_kpt: 0.000756 acc_pose: 0.832707 loss: 0.000756 2022/10/10 19:07:46 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 3:19:22 time: 0.477585 data_time: 0.086635 memory: 6293 loss_kpt: 0.000756 acc_pose: 0.792273 loss: 0.000756 2022/10/10 19:08:06 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 3:19:11 time: 0.409592 data_time: 0.072971 memory: 6293 loss_kpt: 0.000733 acc_pose: 0.830211 loss: 0.000733 2022/10/10 19:08:24 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:08:46 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:18:27 time: 0.439620 data_time: 0.124611 memory: 6293 loss_kpt: 0.000734 acc_pose: 0.827755 loss: 0.000734 2022/10/10 19:09:08 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:18:17 time: 0.431636 data_time: 0.087360 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.776810 loss: 0.000726 2022/10/10 19:09:29 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:18:06 time: 0.420052 data_time: 0.081638 memory: 6293 loss_kpt: 0.000741 acc_pose: 0.763671 loss: 0.000741 2022/10/10 19:09:49 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:17:54 time: 0.398651 data_time: 0.077608 memory: 6293 loss_kpt: 0.000750 acc_pose: 0.794499 loss: 0.000750 2022/10/10 19:09:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:10:08 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:17:42 time: 0.397201 data_time: 0.073095 memory: 6293 loss_kpt: 0.000747 acc_pose: 0.801967 loss: 0.000747 2022/10/10 19:10:24 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:10:46 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:16:59 time: 0.445743 data_time: 0.131097 memory: 6293 loss_kpt: 0.000744 acc_pose: 0.751580 loss: 0.000744 2022/10/10 19:11:08 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:16:49 time: 0.436352 data_time: 0.075993 memory: 6293 loss_kpt: 0.000744 acc_pose: 0.826453 loss: 0.000744 2022/10/10 19:11:31 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:16:41 time: 0.457151 data_time: 0.077936 memory: 6293 loss_kpt: 0.000737 acc_pose: 0.745415 loss: 0.000737 2022/10/10 19:11:51 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:16:29 time: 0.404196 data_time: 0.088295 memory: 6293 loss_kpt: 0.000739 acc_pose: 0.836666 loss: 0.000739 2022/10/10 19:12:12 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:16:17 time: 0.411191 data_time: 0.112183 memory: 6293 loss_kpt: 0.000758 acc_pose: 0.816588 loss: 0.000758 2022/10/10 19:12:30 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:12:30 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/10 19:12:38 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:41 time: 0.117028 data_time: 0.054672 memory: 6293 2022/10/10 19:12:44 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:36 time: 0.118710 data_time: 0.052960 memory: 533 2022/10/10 19:12:50 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:31 time: 0.124168 data_time: 0.061021 memory: 533 2022/10/10 19:12:56 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:24 time: 0.118932 data_time: 0.057096 memory: 533 2022/10/10 19:13:03 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:21 time: 0.135772 data_time: 0.072911 memory: 533 2022/10/10 19:13:09 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:14 time: 0.131006 data_time: 0.067702 memory: 533 2022/10/10 19:13:15 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:06 time: 0.113016 data_time: 0.051937 memory: 533 2022/10/10 19:13:21 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.120302 data_time: 0.060589 memory: 533 2022/10/10 19:13:53 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 19:14:05 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.674246 coco/AP .5: 0.878775 coco/AP .75: 0.752678 coco/AP (M): 0.640261 coco/AP (L): 0.739560 coco/AR: 0.733360 coco/AR .5: 0.921127 coco/AR .75: 0.804943 coco/AR (M): 0.690576 coco/AR (L): 0.793831 2022/10/10 19:14:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_80.pth is removed 2022/10/10 19:14:07 - mmengine - INFO - The best checkpoint with 0.6742 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/10 19:14:28 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:15:32 time: 0.414606 data_time: 0.145196 memory: 6293 loss_kpt: 0.000735 acc_pose: 0.792067 loss: 0.000735 2022/10/10 19:14:50 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:15:23 time: 0.455376 data_time: 0.099706 memory: 6293 loss_kpt: 0.000759 acc_pose: 0.765598 loss: 0.000759 2022/10/10 19:15:11 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:15:12 time: 0.415357 data_time: 0.068741 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.814756 loss: 0.000722 2022/10/10 19:15:31 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:15:00 time: 0.398900 data_time: 0.069092 memory: 6293 loss_kpt: 0.000741 acc_pose: 0.800887 loss: 0.000741 2022/10/10 19:15:52 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:14:49 time: 0.422013 data_time: 0.073421 memory: 6293 loss_kpt: 0.000743 acc_pose: 0.825588 loss: 0.000743 2022/10/10 19:16:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:16:32 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:14:06 time: 0.448946 data_time: 0.089976 memory: 6293 loss_kpt: 0.000729 acc_pose: 0.799831 loss: 0.000729 2022/10/10 19:16:53 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:13:55 time: 0.423907 data_time: 0.065327 memory: 6293 loss_kpt: 0.000732 acc_pose: 0.833377 loss: 0.000732 2022/10/10 19:17:17 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:13:47 time: 0.470487 data_time: 0.077821 memory: 6293 loss_kpt: 0.000750 acc_pose: 0.795368 loss: 0.000750 2022/10/10 19:17:38 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:13:36 time: 0.420719 data_time: 0.075677 memory: 6293 loss_kpt: 0.000736 acc_pose: 0.833391 loss: 0.000736 2022/10/10 19:18:01 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 3:13:27 time: 0.459762 data_time: 0.070331 memory: 6293 loss_kpt: 0.000742 acc_pose: 0.819289 loss: 0.000742 2022/10/10 19:18:17 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:18:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:18:39 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 3:12:44 time: 0.430470 data_time: 0.085045 memory: 6293 loss_kpt: 0.000737 acc_pose: 0.827666 loss: 0.000737 2022/10/10 19:19:00 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 3:12:33 time: 0.426337 data_time: 0.076604 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.794911 loss: 0.000731 2022/10/10 19:19:23 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 3:12:24 time: 0.459848 data_time: 0.144488 memory: 6293 loss_kpt: 0.000741 acc_pose: 0.813714 loss: 0.000741 2022/10/10 19:19:43 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 3:12:11 time: 0.395898 data_time: 0.071207 memory: 6293 loss_kpt: 0.000748 acc_pose: 0.822839 loss: 0.000748 2022/10/10 19:20:05 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 3:12:00 time: 0.434568 data_time: 0.095713 memory: 6293 loss_kpt: 0.000729 acc_pose: 0.791710 loss: 0.000729 2022/10/10 19:20:22 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:20:43 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 3:11:17 time: 0.434266 data_time: 0.098955 memory: 6293 loss_kpt: 0.000735 acc_pose: 0.817331 loss: 0.000735 2022/10/10 19:21:08 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 3:11:10 time: 0.489665 data_time: 0.076015 memory: 6293 loss_kpt: 0.000730 acc_pose: 0.760862 loss: 0.000730 2022/10/10 19:21:31 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 3:11:02 time: 0.469156 data_time: 0.144607 memory: 6293 loss_kpt: 0.000734 acc_pose: 0.748271 loss: 0.000734 2022/10/10 19:21:57 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 3:10:57 time: 0.522208 data_time: 0.108956 memory: 6293 loss_kpt: 0.000746 acc_pose: 0.804632 loss: 0.000746 2022/10/10 19:22:18 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 3:10:45 time: 0.419382 data_time: 0.074649 memory: 6293 loss_kpt: 0.000749 acc_pose: 0.792662 loss: 0.000749 2022/10/10 19:22:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:22:59 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 3:10:03 time: 0.459058 data_time: 0.081287 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.793579 loss: 0.000716 2022/10/10 19:23:20 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 3:09:52 time: 0.423334 data_time: 0.068957 memory: 6293 loss_kpt: 0.000743 acc_pose: 0.787877 loss: 0.000743 2022/10/10 19:23:41 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 3:09:40 time: 0.421687 data_time: 0.068835 memory: 6293 loss_kpt: 0.000740 acc_pose: 0.791652 loss: 0.000740 2022/10/10 19:24:02 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 3:09:29 time: 0.428589 data_time: 0.069630 memory: 6293 loss_kpt: 0.000729 acc_pose: 0.791428 loss: 0.000729 2022/10/10 19:24:22 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 3:09:15 time: 0.381899 data_time: 0.071068 memory: 6293 loss_kpt: 0.000730 acc_pose: 0.799568 loss: 0.000730 2022/10/10 19:24:38 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:25:00 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 3:08:32 time: 0.437972 data_time: 0.088130 memory: 6293 loss_kpt: 0.000741 acc_pose: 0.855010 loss: 0.000741 2022/10/10 19:25:22 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 3:08:21 time: 0.426708 data_time: 0.065375 memory: 6293 loss_kpt: 0.000749 acc_pose: 0.857973 loss: 0.000749 2022/10/10 19:25:43 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 3:08:09 time: 0.420369 data_time: 0.073215 memory: 6293 loss_kpt: 0.000729 acc_pose: 0.760587 loss: 0.000729 2022/10/10 19:25:49 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:26:04 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 3:07:57 time: 0.419624 data_time: 0.073750 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.807711 loss: 0.000721 2022/10/10 19:26:24 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 3:07:45 time: 0.409227 data_time: 0.069363 memory: 6293 loss_kpt: 0.000741 acc_pose: 0.776043 loss: 0.000741 2022/10/10 19:26:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:27:06 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 3:07:03 time: 0.444257 data_time: 0.082857 memory: 6293 loss_kpt: 0.000739 acc_pose: 0.812320 loss: 0.000739 2022/10/10 19:27:26 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 3:06:50 time: 0.409000 data_time: 0.070708 memory: 6293 loss_kpt: 0.000734 acc_pose: 0.812396 loss: 0.000734 2022/10/10 19:27:47 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 3:06:38 time: 0.420224 data_time: 0.066624 memory: 6293 loss_kpt: 0.000724 acc_pose: 0.826543 loss: 0.000724 2022/10/10 19:28:09 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 3:06:28 time: 0.442071 data_time: 0.072668 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.818235 loss: 0.000720 2022/10/10 19:28:30 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 3:06:15 time: 0.407486 data_time: 0.068492 memory: 6293 loss_kpt: 0.000743 acc_pose: 0.815920 loss: 0.000743 2022/10/10 19:28:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:29:06 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 3:05:31 time: 0.415303 data_time: 0.083485 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.840232 loss: 0.000726 2022/10/10 19:29:28 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 3:05:19 time: 0.421622 data_time: 0.065584 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.764021 loss: 0.000731 2022/10/10 19:29:49 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 3:05:08 time: 0.429726 data_time: 0.073596 memory: 6293 loss_kpt: 0.000718 acc_pose: 0.793640 loss: 0.000718 2022/10/10 19:30:09 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 3:04:55 time: 0.406375 data_time: 0.073542 memory: 6293 loss_kpt: 0.000746 acc_pose: 0.829209 loss: 0.000746 2022/10/10 19:30:30 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 3:04:42 time: 0.404405 data_time: 0.093117 memory: 6293 loss_kpt: 0.000743 acc_pose: 0.812552 loss: 0.000743 2022/10/10 19:30:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:31:07 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 3:03:59 time: 0.417005 data_time: 0.100390 memory: 6293 loss_kpt: 0.000745 acc_pose: 0.801486 loss: 0.000745 2022/10/10 19:31:27 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 3:03:46 time: 0.402211 data_time: 0.080025 memory: 6293 loss_kpt: 0.000735 acc_pose: 0.857524 loss: 0.000735 2022/10/10 19:31:47 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 3:03:32 time: 0.401585 data_time: 0.067139 memory: 6293 loss_kpt: 0.000734 acc_pose: 0.800637 loss: 0.000734 2022/10/10 19:32:09 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 3:03:21 time: 0.438363 data_time: 0.089941 memory: 6293 loss_kpt: 0.000742 acc_pose: 0.841632 loss: 0.000742 2022/10/10 19:32:30 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 3:03:08 time: 0.409002 data_time: 0.072776 memory: 6293 loss_kpt: 0.000733 acc_pose: 0.833780 loss: 0.000733 2022/10/10 19:32:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:32:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:33:06 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 3:02:25 time: 0.417064 data_time: 0.097916 memory: 6293 loss_kpt: 0.000746 acc_pose: 0.790491 loss: 0.000746 2022/10/10 19:33:27 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 3:02:13 time: 0.412292 data_time: 0.084671 memory: 6293 loss_kpt: 0.000738 acc_pose: 0.794253 loss: 0.000738 2022/10/10 19:33:48 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 3:02:00 time: 0.423451 data_time: 0.081925 memory: 6293 loss_kpt: 0.000728 acc_pose: 0.832185 loss: 0.000728 2022/10/10 19:34:11 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 3:01:50 time: 0.449062 data_time: 0.068934 memory: 6293 loss_kpt: 0.000729 acc_pose: 0.802090 loss: 0.000729 2022/10/10 19:34:33 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 3:01:39 time: 0.455278 data_time: 0.073021 memory: 6293 loss_kpt: 0.000735 acc_pose: 0.833817 loss: 0.000735 2022/10/10 19:34:52 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:34:52 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/10 19:35:00 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:48 time: 0.136239 data_time: 0.073336 memory: 6293 2022/10/10 19:35:06 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:37 time: 0.121238 data_time: 0.058177 memory: 533 2022/10/10 19:35:13 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:31 time: 0.122919 data_time: 0.054001 memory: 533 2022/10/10 19:35:20 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:28 time: 0.137823 data_time: 0.074685 memory: 533 2022/10/10 19:35:26 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:21 time: 0.135954 data_time: 0.072613 memory: 533 2022/10/10 19:35:33 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:14 time: 0.133912 data_time: 0.072317 memory: 533 2022/10/10 19:35:39 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:06 time: 0.111985 data_time: 0.049494 memory: 533 2022/10/10 19:35:45 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.134805 data_time: 0.073773 memory: 533 2022/10/10 19:36:16 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 19:36:28 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.674165 coco/AP .5: 0.879310 coco/AP .75: 0.751957 coco/AP (M): 0.641733 coco/AP (L): 0.737376 coco/AR: 0.733344 coco/AR .5: 0.920183 coco/AR .75: 0.805573 coco/AR (M): 0.691232 coco/AR (L): 0.793385 2022/10/10 19:36:50 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 3:00:58 time: 0.444955 data_time: 0.139095 memory: 6293 loss_kpt: 0.000728 acc_pose: 0.843426 loss: 0.000728 2022/10/10 19:37:09 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 3:00:43 time: 0.379384 data_time: 0.069986 memory: 6293 loss_kpt: 0.000738 acc_pose: 0.818209 loss: 0.000738 2022/10/10 19:37:30 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 3:00:30 time: 0.410110 data_time: 0.071876 memory: 6293 loss_kpt: 0.000727 acc_pose: 0.744437 loss: 0.000727 2022/10/10 19:37:49 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 3:00:16 time: 0.376280 data_time: 0.074501 memory: 6293 loss_kpt: 0.000742 acc_pose: 0.816623 loss: 0.000742 2022/10/10 19:38:08 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 3:00:01 time: 0.381357 data_time: 0.068652 memory: 6293 loss_kpt: 0.000732 acc_pose: 0.802513 loss: 0.000732 2022/10/10 19:38:25 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:38:46 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 2:59:18 time: 0.416987 data_time: 0.086316 memory: 6293 loss_kpt: 0.000732 acc_pose: 0.774943 loss: 0.000732 2022/10/10 19:39:08 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 2:59:07 time: 0.440881 data_time: 0.073061 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.782331 loss: 0.000720 2022/10/10 19:39:28 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 2:58:53 time: 0.396942 data_time: 0.067902 memory: 6293 loss_kpt: 0.000713 acc_pose: 0.781681 loss: 0.000713 2022/10/10 19:39:45 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:58:37 time: 0.346312 data_time: 0.070730 memory: 6293 loss_kpt: 0.000748 acc_pose: 0.847669 loss: 0.000748 2022/10/10 19:40:05 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:58:23 time: 0.386976 data_time: 0.070898 memory: 6293 loss_kpt: 0.000729 acc_pose: 0.810852 loss: 0.000729 2022/10/10 19:40:20 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:40:40 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:57:40 time: 0.406989 data_time: 0.093541 memory: 6293 loss_kpt: 0.000736 acc_pose: 0.825514 loss: 0.000736 2022/10/10 19:41:00 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:57:26 time: 0.407589 data_time: 0.080692 memory: 6293 loss_kpt: 0.000732 acc_pose: 0.824222 loss: 0.000732 2022/10/10 19:41:06 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:41:21 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:57:13 time: 0.406106 data_time: 0.074625 memory: 6293 loss_kpt: 0.000737 acc_pose: 0.810781 loss: 0.000737 2022/10/10 19:41:40 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:56:58 time: 0.378633 data_time: 0.073934 memory: 6293 loss_kpt: 0.000730 acc_pose: 0.827997 loss: 0.000730 2022/10/10 19:42:00 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:56:45 time: 0.398676 data_time: 0.067643 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.842793 loss: 0.000719 2022/10/10 19:42:17 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:42:37 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:56:01 time: 0.386745 data_time: 0.081021 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.806853 loss: 0.000721 2022/10/10 19:42:59 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:55:49 time: 0.446045 data_time: 0.073318 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.815477 loss: 0.000726 2022/10/10 19:43:20 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:55:37 time: 0.416783 data_time: 0.075117 memory: 6293 loss_kpt: 0.000736 acc_pose: 0.828729 loss: 0.000736 2022/10/10 19:43:39 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:55:22 time: 0.376679 data_time: 0.068651 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.766522 loss: 0.000719 2022/10/10 19:43:58 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:55:07 time: 0.385115 data_time: 0.076187 memory: 6293 loss_kpt: 0.000723 acc_pose: 0.854412 loss: 0.000723 2022/10/10 19:44:15 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:44:35 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:54:24 time: 0.398859 data_time: 0.084687 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.818482 loss: 0.000726 2022/10/10 19:44:56 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:54:12 time: 0.422501 data_time: 0.079268 memory: 6293 loss_kpt: 0.000736 acc_pose: 0.845931 loss: 0.000736 2022/10/10 19:45:16 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:53:58 time: 0.401175 data_time: 0.101284 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.817373 loss: 0.000731 2022/10/10 19:45:36 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:53:44 time: 0.402138 data_time: 0.086639 memory: 6293 loss_kpt: 0.000734 acc_pose: 0.801711 loss: 0.000734 2022/10/10 19:45:57 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:53:31 time: 0.412962 data_time: 0.074941 memory: 6293 loss_kpt: 0.000728 acc_pose: 0.800262 loss: 0.000728 2022/10/10 19:46:13 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:46:35 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:52:50 time: 0.427770 data_time: 0.098800 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.818788 loss: 0.000721 2022/10/10 19:46:55 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:52:36 time: 0.401301 data_time: 0.067490 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.809871 loss: 0.000722 2022/10/10 19:47:15 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:52:22 time: 0.397999 data_time: 0.083252 memory: 6293 loss_kpt: 0.000718 acc_pose: 0.815836 loss: 0.000718 2022/10/10 19:47:35 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:52:08 time: 0.397206 data_time: 0.066956 memory: 6293 loss_kpt: 0.000733 acc_pose: 0.782544 loss: 0.000733 2022/10/10 19:47:48 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:47:54 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:51:54 time: 0.383574 data_time: 0.071987 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.804811 loss: 0.000726 2022/10/10 19:48:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:48:33 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:51:13 time: 0.447434 data_time: 0.084897 memory: 6293 loss_kpt: 0.000728 acc_pose: 0.856595 loss: 0.000728 2022/10/10 19:48:51 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:50:58 time: 0.365251 data_time: 0.078188 memory: 6293 loss_kpt: 0.000730 acc_pose: 0.828198 loss: 0.000730 2022/10/10 19:49:09 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:50:42 time: 0.363939 data_time: 0.073192 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.840452 loss: 0.000716 2022/10/10 19:49:30 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:50:29 time: 0.418540 data_time: 0.068481 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.823795 loss: 0.000720 2022/10/10 19:49:51 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:50:16 time: 0.412025 data_time: 0.078406 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.792855 loss: 0.000721 2022/10/10 19:50:08 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:50:28 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:49:34 time: 0.408416 data_time: 0.079041 memory: 6293 loss_kpt: 0.000730 acc_pose: 0.851405 loss: 0.000730 2022/10/10 19:50:51 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:49:22 time: 0.443583 data_time: 0.068542 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.862389 loss: 0.000720 2022/10/10 19:51:11 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:49:09 time: 0.412316 data_time: 0.072850 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.768774 loss: 0.000716 2022/10/10 19:51:32 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:48:55 time: 0.410301 data_time: 0.083947 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.807675 loss: 0.000716 2022/10/10 19:51:51 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:48:41 time: 0.387878 data_time: 0.071410 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.880354 loss: 0.000719 2022/10/10 19:52:09 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:52:31 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:48:00 time: 0.442216 data_time: 0.090560 memory: 6293 loss_kpt: 0.000723 acc_pose: 0.786276 loss: 0.000723 2022/10/10 19:52:50 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:47:46 time: 0.386204 data_time: 0.071710 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.834715 loss: 0.000722 2022/10/10 19:53:09 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:47:31 time: 0.379459 data_time: 0.073185 memory: 6293 loss_kpt: 0.000714 acc_pose: 0.760669 loss: 0.000714 2022/10/10 19:53:28 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:47:16 time: 0.377425 data_time: 0.073568 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.825436 loss: 0.000720 2022/10/10 19:53:46 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:47:00 time: 0.366953 data_time: 0.065111 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.809546 loss: 0.000719 2022/10/10 19:54:02 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:54:23 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:46:19 time: 0.427194 data_time: 0.091203 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.822143 loss: 0.000731 2022/10/10 19:54:28 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:54:44 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:46:06 time: 0.415220 data_time: 0.095434 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.850346 loss: 0.000708 2022/10/10 19:55:04 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:45:52 time: 0.406794 data_time: 0.073340 memory: 6293 loss_kpt: 0.000713 acc_pose: 0.789307 loss: 0.000713 2022/10/10 19:55:24 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:45:38 time: 0.400881 data_time: 0.071607 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.839916 loss: 0.000722 2022/10/10 19:55:44 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:45:24 time: 0.387890 data_time: 0.076514 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.778908 loss: 0.000719 2022/10/10 19:56:01 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:56:01 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/10 19:56:09 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:43 time: 0.121965 data_time: 0.058499 memory: 6293 2022/10/10 19:56:15 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:35 time: 0.114415 data_time: 0.051096 memory: 533 2022/10/10 19:56:21 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:30 time: 0.118721 data_time: 0.052286 memory: 533 2022/10/10 19:56:27 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:24 time: 0.119949 data_time: 0.056264 memory: 533 2022/10/10 19:56:33 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:19 time: 0.127074 data_time: 0.065051 memory: 533 2022/10/10 19:56:39 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:13 time: 0.125181 data_time: 0.063721 memory: 533 2022/10/10 19:56:46 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:06 time: 0.122437 data_time: 0.058954 memory: 533 2022/10/10 19:56:52 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.125828 data_time: 0.063862 memory: 533 2022/10/10 19:57:22 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 19:57:34 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.680213 coco/AP .5: 0.879807 coco/AP .75: 0.764389 coco/AP (M): 0.648675 coco/AP (L): 0.743888 coco/AR: 0.738476 coco/AR .5: 0.921285 coco/AR .75: 0.814232 coco/AR (M): 0.697514 coco/AR (L): 0.797362 2022/10/10 19:57:34 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_90.pth is removed 2022/10/10 19:57:36 - mmengine - INFO - The best checkpoint with 0.6802 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/10 19:57:56 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:44:42 time: 0.404971 data_time: 0.085382 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.867875 loss: 0.000709 2022/10/10 19:58:17 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:44:28 time: 0.404879 data_time: 0.063930 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.825168 loss: 0.000722 2022/10/10 19:58:36 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:44:14 time: 0.393998 data_time: 0.082846 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.847542 loss: 0.000731 2022/10/10 19:58:56 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:43:59 time: 0.386754 data_time: 0.077129 memory: 6293 loss_kpt: 0.000730 acc_pose: 0.826172 loss: 0.000730 2022/10/10 19:59:15 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:43:44 time: 0.388814 data_time: 0.077714 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.818791 loss: 0.000722 2022/10/10 19:59:32 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 19:59:51 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:43:02 time: 0.383832 data_time: 0.086626 memory: 6293 loss_kpt: 0.000725 acc_pose: 0.824736 loss: 0.000725 2022/10/10 20:00:10 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:42:47 time: 0.385854 data_time: 0.069411 memory: 6293 loss_kpt: 0.000724 acc_pose: 0.807790 loss: 0.000724 2022/10/10 20:00:30 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:42:33 time: 0.405867 data_time: 0.080284 memory: 6293 loss_kpt: 0.000718 acc_pose: 0.806502 loss: 0.000718 2022/10/10 20:00:49 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:42:18 time: 0.363697 data_time: 0.065715 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.835684 loss: 0.000726 2022/10/10 20:01:08 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:42:03 time: 0.396242 data_time: 0.077106 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.787997 loss: 0.000722 2022/10/10 20:01:25 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:01:46 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:41:23 time: 0.423738 data_time: 0.087580 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.827941 loss: 0.000722 2022/10/10 20:02:06 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:41:09 time: 0.402796 data_time: 0.080877 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.826222 loss: 0.000726 2022/10/10 20:02:27 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:40:55 time: 0.404673 data_time: 0.072361 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.792648 loss: 0.000722 2022/10/10 20:02:39 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:02:46 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:40:40 time: 0.381250 data_time: 0.068807 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.757563 loss: 0.000703 2022/10/10 20:03:04 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:40:24 time: 0.365364 data_time: 0.070139 memory: 6293 loss_kpt: 0.000738 acc_pose: 0.842117 loss: 0.000738 2022/10/10 20:03:20 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:03:39 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:39:42 time: 0.381677 data_time: 0.083146 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.845049 loss: 0.000720 2022/10/10 20:03:57 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:39:26 time: 0.357992 data_time: 0.078909 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.818663 loss: 0.000708 2022/10/10 20:04:16 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:39:11 time: 0.378662 data_time: 0.081456 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.832248 loss: 0.000720 2022/10/10 20:04:34 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:38:55 time: 0.351872 data_time: 0.078183 memory: 6293 loss_kpt: 0.000712 acc_pose: 0.818353 loss: 0.000712 2022/10/10 20:04:51 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:38:38 time: 0.357162 data_time: 0.074727 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.787339 loss: 0.000731 2022/10/10 20:05:06 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:05:25 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:37:57 time: 0.379934 data_time: 0.088981 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.837252 loss: 0.000701 2022/10/10 20:05:43 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:37:40 time: 0.346986 data_time: 0.076019 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.836192 loss: 0.000710 2022/10/10 20:06:00 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:37:24 time: 0.347770 data_time: 0.078138 memory: 6293 loss_kpt: 0.000711 acc_pose: 0.846370 loss: 0.000711 2022/10/10 20:06:21 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:37:10 time: 0.410257 data_time: 0.068292 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.809509 loss: 0.000722 2022/10/10 20:06:40 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:36:55 time: 0.378241 data_time: 0.077478 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.818453 loss: 0.000721 2022/10/10 20:06:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:07:17 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:36:15 time: 0.424481 data_time: 0.080090 memory: 6293 loss_kpt: 0.000724 acc_pose: 0.787174 loss: 0.000724 2022/10/10 20:07:37 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:36:00 time: 0.389783 data_time: 0.070167 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.849164 loss: 0.000702 2022/10/10 20:07:55 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:35:44 time: 0.363490 data_time: 0.072026 memory: 6293 loss_kpt: 0.000725 acc_pose: 0.775537 loss: 0.000725 2022/10/10 20:08:15 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:35:30 time: 0.406340 data_time: 0.069400 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.841781 loss: 0.000720 2022/10/10 20:08:36 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:35:16 time: 0.409731 data_time: 0.066544 memory: 6293 loss_kpt: 0.000732 acc_pose: 0.785795 loss: 0.000732 2022/10/10 20:08:53 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:08:58 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:09:12 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:34:35 time: 0.393432 data_time: 0.082447 memory: 6293 loss_kpt: 0.000723 acc_pose: 0.848110 loss: 0.000723 2022/10/10 20:09:31 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:34:20 time: 0.374861 data_time: 0.074399 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.777738 loss: 0.000705 2022/10/10 20:09:51 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:34:06 time: 0.401818 data_time: 0.074030 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.781983 loss: 0.000722 2022/10/10 20:10:11 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:33:51 time: 0.391670 data_time: 0.089978 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.832878 loss: 0.000721 2022/10/10 20:10:30 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:33:36 time: 0.393652 data_time: 0.075799 memory: 6293 loss_kpt: 0.000713 acc_pose: 0.841047 loss: 0.000713 2022/10/10 20:10:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:11:05 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:32:55 time: 0.380977 data_time: 0.081216 memory: 6293 loss_kpt: 0.000712 acc_pose: 0.800542 loss: 0.000712 2022/10/10 20:11:24 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:32:39 time: 0.367346 data_time: 0.067914 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.823088 loss: 0.000716 2022/10/10 20:11:45 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:32:26 time: 0.434048 data_time: 0.069286 memory: 6293 loss_kpt: 0.000731 acc_pose: 0.827388 loss: 0.000731 2022/10/10 20:12:07 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:32:13 time: 0.437775 data_time: 0.082166 memory: 6293 loss_kpt: 0.000727 acc_pose: 0.815305 loss: 0.000727 2022/10/10 20:12:27 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:31:59 time: 0.388163 data_time: 0.072904 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.837031 loss: 0.000716 2022/10/10 20:12:42 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:13:03 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:31:19 time: 0.406441 data_time: 0.088257 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.820178 loss: 0.000720 2022/10/10 20:13:23 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:31:04 time: 0.400341 data_time: 0.072610 memory: 6293 loss_kpt: 0.000712 acc_pose: 0.798570 loss: 0.000712 2022/10/10 20:13:42 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:30:49 time: 0.385375 data_time: 0.075789 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.777220 loss: 0.000705 2022/10/10 20:14:01 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:30:34 time: 0.386938 data_time: 0.079266 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.854130 loss: 0.000716 2022/10/10 20:14:20 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:30:18 time: 0.364218 data_time: 0.071216 memory: 6293 loss_kpt: 0.000723 acc_pose: 0.779772 loss: 0.000723 2022/10/10 20:14:35 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:14:56 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:29:39 time: 0.414214 data_time: 0.091109 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.830207 loss: 0.000716 2022/10/10 20:15:15 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:29:24 time: 0.387319 data_time: 0.067833 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.838614 loss: 0.000705 2022/10/10 20:15:28 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:15:34 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:29:09 time: 0.382530 data_time: 0.068486 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.787060 loss: 0.000705 2022/10/10 20:15:53 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:28:53 time: 0.379879 data_time: 0.071744 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.801798 loss: 0.000708 2022/10/10 20:16:13 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:28:39 time: 0.399958 data_time: 0.075672 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.827297 loss: 0.000709 2022/10/10 20:16:29 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:16:29 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/10 20:16:37 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:43 time: 0.121541 data_time: 0.056991 memory: 6293 2022/10/10 20:16:44 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:38 time: 0.125095 data_time: 0.062185 memory: 533 2022/10/10 20:16:49 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:28 time: 0.109487 data_time: 0.046752 memory: 533 2022/10/10 20:16:56 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:30 time: 0.145580 data_time: 0.080996 memory: 533 2022/10/10 20:17:03 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:20 time: 0.130006 data_time: 0.065619 memory: 533 2022/10/10 20:17:08 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:11 time: 0.108025 data_time: 0.044863 memory: 533 2022/10/10 20:17:14 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.121106 data_time: 0.058582 memory: 533 2022/10/10 20:17:20 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.114262 data_time: 0.050997 memory: 533 2022/10/10 20:17:51 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 20:18:03 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.679613 coco/AP .5: 0.878723 coco/AP .75: 0.759449 coco/AP (M): 0.647371 coco/AP (L): 0.744579 coco/AR: 0.739106 coco/AR .5: 0.920812 coco/AR .75: 0.811241 coco/AR (M): 0.696613 coco/AR (L): 0.799926 2022/10/10 20:18:25 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:28:00 time: 0.437731 data_time: 0.078604 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.803258 loss: 0.000722 2022/10/10 20:18:44 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:27:45 time: 0.381776 data_time: 0.072749 memory: 6293 loss_kpt: 0.000717 acc_pose: 0.808408 loss: 0.000717 2022/10/10 20:19:03 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:27:29 time: 0.373952 data_time: 0.066352 memory: 6293 loss_kpt: 0.000718 acc_pose: 0.775099 loss: 0.000718 2022/10/10 20:19:21 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:27:14 time: 0.371232 data_time: 0.069339 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.821071 loss: 0.000716 2022/10/10 20:19:40 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:26:59 time: 0.384703 data_time: 0.075488 memory: 6293 loss_kpt: 0.000721 acc_pose: 0.771667 loss: 0.000721 2022/10/10 20:19:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:20:18 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:26:20 time: 0.439512 data_time: 0.084192 memory: 6293 loss_kpt: 0.000714 acc_pose: 0.845308 loss: 0.000714 2022/10/10 20:20:37 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:26:05 time: 0.372921 data_time: 0.067759 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.854303 loss: 0.000719 2022/10/10 20:20:56 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:25:49 time: 0.377715 data_time: 0.065279 memory: 6293 loss_kpt: 0.000711 acc_pose: 0.737849 loss: 0.000711 2022/10/10 20:21:16 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:25:35 time: 0.401371 data_time: 0.090853 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.877491 loss: 0.000703 2022/10/10 20:21:37 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:25:21 time: 0.416171 data_time: 0.096441 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.769613 loss: 0.000708 2022/10/10 20:21:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:22:13 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:24:40 time: 0.378222 data_time: 0.093018 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.813192 loss: 0.000698 2022/10/10 20:22:33 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:24:26 time: 0.404665 data_time: 0.083889 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.837194 loss: 0.000701 2022/10/10 20:22:53 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:24:12 time: 0.405140 data_time: 0.072230 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.833336 loss: 0.000710 2022/10/10 20:23:15 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:23:58 time: 0.433728 data_time: 0.072221 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.803062 loss: 0.000720 2022/10/10 20:23:36 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:23:44 time: 0.430746 data_time: 0.077269 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.810410 loss: 0.000704 2022/10/10 20:23:38 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:23:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:24:15 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:23:06 time: 0.431348 data_time: 0.094448 memory: 6293 loss_kpt: 0.000706 acc_pose: 0.828871 loss: 0.000706 2022/10/10 20:24:37 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:22:53 time: 0.435245 data_time: 0.076206 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.831404 loss: 0.000708 2022/10/10 20:24:56 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:22:38 time: 0.389311 data_time: 0.073587 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.820719 loss: 0.000722 2022/10/10 20:25:18 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:22:24 time: 0.425193 data_time: 0.074893 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.835663 loss: 0.000708 2022/10/10 20:25:37 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:22:09 time: 0.386653 data_time: 0.075738 memory: 6293 loss_kpt: 0.000718 acc_pose: 0.810308 loss: 0.000718 2022/10/10 20:25:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:26:15 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:21:30 time: 0.418242 data_time: 0.083827 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.773919 loss: 0.000704 2022/10/10 20:26:35 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:21:15 time: 0.381290 data_time: 0.078667 memory: 6293 loss_kpt: 0.000734 acc_pose: 0.814967 loss: 0.000734 2022/10/10 20:26:54 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:21:00 time: 0.394713 data_time: 0.072143 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.827314 loss: 0.000700 2022/10/10 20:27:16 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:20:46 time: 0.434563 data_time: 0.070981 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.787604 loss: 0.000708 2022/10/10 20:27:36 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:20:31 time: 0.405668 data_time: 0.076884 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.861378 loss: 0.000710 2022/10/10 20:27:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:28:15 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:19:53 time: 0.428807 data_time: 0.077300 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.779852 loss: 0.000700 2022/10/10 20:28:35 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:19:38 time: 0.384898 data_time: 0.069533 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.784287 loss: 0.000726 2022/10/10 20:28:54 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:19:23 time: 0.381167 data_time: 0.076586 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.844276 loss: 0.000710 2022/10/10 20:29:13 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:19:07 time: 0.383098 data_time: 0.079956 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.801539 loss: 0.000699 2022/10/10 20:29:32 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:18:52 time: 0.390121 data_time: 0.087972 memory: 6293 loss_kpt: 0.000715 acc_pose: 0.788588 loss: 0.000715 2022/10/10 20:29:50 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:30:11 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:18:14 time: 0.424541 data_time: 0.094885 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.814623 loss: 0.000707 2022/10/10 20:30:24 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:30:31 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:17:59 time: 0.399539 data_time: 0.075729 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.845689 loss: 0.000703 2022/10/10 20:30:50 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:17:43 time: 0.375419 data_time: 0.071579 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.815826 loss: 0.000703 2022/10/10 20:31:10 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:17:29 time: 0.405822 data_time: 0.082355 memory: 6293 loss_kpt: 0.000720 acc_pose: 0.826393 loss: 0.000720 2022/10/10 20:31:30 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:17:14 time: 0.393174 data_time: 0.067582 memory: 6293 loss_kpt: 0.000715 acc_pose: 0.875208 loss: 0.000715 2022/10/10 20:31:46 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:32:07 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:16:35 time: 0.423031 data_time: 0.082871 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.838906 loss: 0.000701 2022/10/10 20:32:26 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:16:20 time: 0.380216 data_time: 0.070357 memory: 6293 loss_kpt: 0.000719 acc_pose: 0.811325 loss: 0.000719 2022/10/10 20:32:46 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:16:05 time: 0.402386 data_time: 0.072480 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.875033 loss: 0.000707 2022/10/10 20:33:05 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:15:49 time: 0.370744 data_time: 0.079536 memory: 6293 loss_kpt: 0.000713 acc_pose: 0.835181 loss: 0.000713 2022/10/10 20:33:25 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:15:35 time: 0.403834 data_time: 0.066584 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.808711 loss: 0.000709 2022/10/10 20:33:41 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:34:03 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:14:57 time: 0.438295 data_time: 0.082327 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.789325 loss: 0.000710 2022/10/10 20:34:22 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:14:41 time: 0.381161 data_time: 0.066269 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.831276 loss: 0.000699 2022/10/10 20:34:42 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:14:26 time: 0.392283 data_time: 0.076038 memory: 6293 loss_kpt: 0.000694 acc_pose: 0.814339 loss: 0.000694 2022/10/10 20:35:01 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:14:11 time: 0.383959 data_time: 0.085861 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.792905 loss: 0.000710 2022/10/10 20:35:21 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:13:56 time: 0.398698 data_time: 0.069358 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.810552 loss: 0.000697 2022/10/10 20:35:37 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:35:59 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:13:18 time: 0.437389 data_time: 0.094289 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.786161 loss: 0.000709 2022/10/10 20:36:18 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:13:03 time: 0.373843 data_time: 0.077195 memory: 6293 loss_kpt: 0.000695 acc_pose: 0.794136 loss: 0.000695 2022/10/10 20:36:38 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:12:48 time: 0.402443 data_time: 0.089134 memory: 6293 loss_kpt: 0.000723 acc_pose: 0.790037 loss: 0.000723 2022/10/10 20:36:57 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:12:33 time: 0.390308 data_time: 0.117379 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.804911 loss: 0.000707 2022/10/10 20:36:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:37:17 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:12:18 time: 0.399844 data_time: 0.074931 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.823875 loss: 0.000698 2022/10/10 20:37:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:37:34 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/10 20:37:43 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:47 time: 0.133939 data_time: 0.071048 memory: 6293 2022/10/10 20:37:48 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:33 time: 0.110724 data_time: 0.048354 memory: 533 2022/10/10 20:37:54 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:30 time: 0.117555 data_time: 0.055837 memory: 533 2022/10/10 20:37:59 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:21 time: 0.105511 data_time: 0.043851 memory: 533 2022/10/10 20:38:06 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:22 time: 0.142482 data_time: 0.078237 memory: 533 2022/10/10 20:38:12 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:12 time: 0.119863 data_time: 0.057089 memory: 533 2022/10/10 20:38:18 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.120027 data_time: 0.056834 memory: 533 2022/10/10 20:38:24 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.107036 data_time: 0.046441 memory: 533 2022/10/10 20:38:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 20:39:06 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.686489 coco/AP .5: 0.884086 coco/AP .75: 0.764576 coco/AP (M): 0.655453 coco/AP (L): 0.750059 coco/AR: 0.744679 coco/AR .5: 0.924591 coco/AR .75: 0.815649 coco/AR (M): 0.701994 coco/AR (L): 0.805611 2022/10/10 20:39:06 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_110.pth is removed 2022/10/10 20:39:08 - mmengine - INFO - The best checkpoint with 0.6865 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/10 20:39:28 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:11:39 time: 0.407140 data_time: 0.085772 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.767221 loss: 0.000703 2022/10/10 20:39:48 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:11:24 time: 0.403777 data_time: 0.072647 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.803877 loss: 0.000692 2022/10/10 20:40:08 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:11:09 time: 0.397935 data_time: 0.063410 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.802465 loss: 0.000697 2022/10/10 20:40:27 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 2:10:54 time: 0.379170 data_time: 0.071140 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.819052 loss: 0.000702 2022/10/10 20:40:46 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 2:10:38 time: 0.370217 data_time: 0.073401 memory: 6293 loss_kpt: 0.000706 acc_pose: 0.840533 loss: 0.000706 2022/10/10 20:41:02 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:41:23 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 2:10:00 time: 0.427178 data_time: 0.087804 memory: 6293 loss_kpt: 0.000726 acc_pose: 0.775201 loss: 0.000726 2022/10/10 20:41:43 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 2:09:45 time: 0.386151 data_time: 0.082844 memory: 6293 loss_kpt: 0.000694 acc_pose: 0.845980 loss: 0.000694 2022/10/10 20:42:03 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 2:09:30 time: 0.410040 data_time: 0.071590 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.784782 loss: 0.000704 2022/10/10 20:42:23 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 2:09:15 time: 0.400654 data_time: 0.071091 memory: 6293 loss_kpt: 0.000694 acc_pose: 0.786467 loss: 0.000694 2022/10/10 20:42:44 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 2:09:00 time: 0.409021 data_time: 0.069996 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.805016 loss: 0.000709 2022/10/10 20:42:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:43:20 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 2:08:23 time: 0.424841 data_time: 0.089519 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.839921 loss: 0.000702 2022/10/10 20:43:40 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 2:08:07 time: 0.387385 data_time: 0.076025 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.868149 loss: 0.000703 2022/10/10 20:44:01 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 2:07:53 time: 0.426109 data_time: 0.068435 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.845520 loss: 0.000701 2022/10/10 20:44:21 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 2:07:38 time: 0.396185 data_time: 0.068981 memory: 6293 loss_kpt: 0.000722 acc_pose: 0.847929 loss: 0.000722 2022/10/10 20:44:42 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 2:07:23 time: 0.414143 data_time: 0.078862 memory: 6293 loss_kpt: 0.000715 acc_pose: 0.828011 loss: 0.000715 2022/10/10 20:44:58 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:45:11 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:45:18 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 2:06:45 time: 0.401133 data_time: 0.088921 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.847817 loss: 0.000702 2022/10/10 20:45:38 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 2:06:30 time: 0.392599 data_time: 0.068316 memory: 6293 loss_kpt: 0.000713 acc_pose: 0.771322 loss: 0.000713 2022/10/10 20:45:58 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 2:06:15 time: 0.414523 data_time: 0.070121 memory: 6293 loss_kpt: 0.000715 acc_pose: 0.823820 loss: 0.000715 2022/10/10 20:46:17 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 2:05:59 time: 0.377790 data_time: 0.067537 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.808304 loss: 0.000701 2022/10/10 20:46:35 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 2:05:43 time: 0.354509 data_time: 0.063907 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.842672 loss: 0.000709 2022/10/10 20:46:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:47:16 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 2:05:06 time: 0.431527 data_time: 0.083038 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.841831 loss: 0.000698 2022/10/10 20:47:36 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 2:04:51 time: 0.399058 data_time: 0.062243 memory: 6293 loss_kpt: 0.000716 acc_pose: 0.779153 loss: 0.000716 2022/10/10 20:47:55 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 2:04:36 time: 0.396042 data_time: 0.066369 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.752703 loss: 0.000709 2022/10/10 20:48:15 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 2:04:20 time: 0.385064 data_time: 0.070912 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.797093 loss: 0.000710 2022/10/10 20:48:34 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 2:04:05 time: 0.391659 data_time: 0.074506 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.802081 loss: 0.000704 2022/10/10 20:48:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:49:11 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 2:03:27 time: 0.413993 data_time: 0.084438 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.839605 loss: 0.000703 2022/10/10 20:49:30 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 2:03:11 time: 0.369764 data_time: 0.069220 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.803644 loss: 0.000690 2022/10/10 20:49:50 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 2:02:56 time: 0.392044 data_time: 0.076753 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.815486 loss: 0.000698 2022/10/10 20:50:11 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 2:02:41 time: 0.422394 data_time: 0.076198 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.762616 loss: 0.000705 2022/10/10 20:50:30 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 2:02:26 time: 0.390704 data_time: 0.071011 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.789748 loss: 0.000705 2022/10/10 20:50:47 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:51:09 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 2:01:49 time: 0.437255 data_time: 0.087222 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.820561 loss: 0.000698 2022/10/10 20:51:28 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 2:01:34 time: 0.394137 data_time: 0.068376 memory: 6293 loss_kpt: 0.000706 acc_pose: 0.806085 loss: 0.000706 2022/10/10 20:51:47 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 2:01:18 time: 0.377483 data_time: 0.064029 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.804806 loss: 0.000691 2022/10/10 20:51:48 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:52:07 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 2:01:02 time: 0.393786 data_time: 0.086636 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.777147 loss: 0.000701 2022/10/10 20:52:27 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 2:00:47 time: 0.390196 data_time: 0.078563 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.825674 loss: 0.000702 2022/10/10 20:52:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:53:06 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 2:00:10 time: 0.435424 data_time: 0.090171 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.819736 loss: 0.000703 2022/10/10 20:53:26 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 1:59:55 time: 0.398105 data_time: 0.089287 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.845802 loss: 0.000697 2022/10/10 20:53:45 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:59:39 time: 0.385008 data_time: 0.063989 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.878743 loss: 0.000701 2022/10/10 20:54:06 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:59:25 time: 0.415476 data_time: 0.079793 memory: 6293 loss_kpt: 0.000695 acc_pose: 0.837170 loss: 0.000695 2022/10/10 20:54:26 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:59:09 time: 0.398763 data_time: 0.109495 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.822141 loss: 0.000699 2022/10/10 20:54:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:55:05 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:58:32 time: 0.420260 data_time: 0.103528 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.809823 loss: 0.000697 2022/10/10 20:55:25 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:58:17 time: 0.405297 data_time: 0.071362 memory: 6293 loss_kpt: 0.000689 acc_pose: 0.850440 loss: 0.000689 2022/10/10 20:55:44 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:58:01 time: 0.381009 data_time: 0.071104 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.792819 loss: 0.000701 2022/10/10 20:56:04 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:57:46 time: 0.392158 data_time: 0.071566 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.819651 loss: 0.000698 2022/10/10 20:56:23 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:57:30 time: 0.382980 data_time: 0.073187 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.822174 loss: 0.000699 2022/10/10 20:56:40 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:57:00 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:56:53 time: 0.408288 data_time: 0.112167 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.800162 loss: 0.000699 2022/10/10 20:57:20 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:56:38 time: 0.397507 data_time: 0.075416 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.872680 loss: 0.000707 2022/10/10 20:57:39 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:56:22 time: 0.372270 data_time: 0.073748 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.846011 loss: 0.000700 2022/10/10 20:57:58 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:56:06 time: 0.376861 data_time: 0.077727 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.823365 loss: 0.000705 2022/10/10 20:58:18 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:55:50 time: 0.397455 data_time: 0.071063 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.779357 loss: 0.000704 2022/10/10 20:58:27 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:58:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 20:58:34 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/10 20:58:42 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:46 time: 0.129955 data_time: 0.066082 memory: 6293 2022/10/10 20:58:48 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:34 time: 0.113045 data_time: 0.051955 memory: 533 2022/10/10 20:58:54 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:30 time: 0.118588 data_time: 0.054883 memory: 533 2022/10/10 20:59:00 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:23 time: 0.112368 data_time: 0.049593 memory: 533 2022/10/10 20:59:05 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:18 time: 0.117351 data_time: 0.050245 memory: 533 2022/10/10 20:59:11 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:12 time: 0.114698 data_time: 0.051036 memory: 533 2022/10/10 20:59:18 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:07 time: 0.138011 data_time: 0.074985 memory: 533 2022/10/10 20:59:24 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.108946 data_time: 0.048299 memory: 533 2022/10/10 20:59:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 21:00:06 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.682680 coco/AP .5: 0.880746 coco/AP .75: 0.764007 coco/AP (M): 0.652623 coco/AP (L): 0.743682 coco/AR: 0.741892 coco/AR .5: 0.923174 coco/AR .75: 0.814704 coco/AR (M): 0.701366 coco/AR (L): 0.799703 2022/10/10 21:00:27 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:55:14 time: 0.420834 data_time: 0.084801 memory: 6293 loss_kpt: 0.000712 acc_pose: 0.811670 loss: 0.000712 2022/10/10 21:00:46 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:54:58 time: 0.374389 data_time: 0.072222 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.846475 loss: 0.000697 2022/10/10 21:01:05 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:54:42 time: 0.389892 data_time: 0.082308 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.791164 loss: 0.000697 2022/10/10 21:01:25 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:54:27 time: 0.408586 data_time: 0.077679 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.796563 loss: 0.000697 2022/10/10 21:01:46 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:54:12 time: 0.413007 data_time: 0.075053 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.838343 loss: 0.000699 2022/10/10 21:02:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:02:25 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:53:35 time: 0.415186 data_time: 0.078382 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.785119 loss: 0.000707 2022/10/10 21:02:45 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:53:20 time: 0.407173 data_time: 0.075015 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.807289 loss: 0.000691 2022/10/10 21:03:04 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:53:04 time: 0.383900 data_time: 0.086432 memory: 6293 loss_kpt: 0.000695 acc_pose: 0.838793 loss: 0.000695 2022/10/10 21:03:23 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:52:48 time: 0.379371 data_time: 0.077305 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.817227 loss: 0.000700 2022/10/10 21:03:43 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:52:33 time: 0.394219 data_time: 0.073446 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.833984 loss: 0.000703 2022/10/10 21:03:58 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:04:19 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:51:56 time: 0.411973 data_time: 0.107042 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.813379 loss: 0.000701 2022/10/10 21:04:40 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:51:41 time: 0.415288 data_time: 0.076649 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.827976 loss: 0.000702 2022/10/10 21:05:00 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:51:26 time: 0.400761 data_time: 0.072722 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.791900 loss: 0.000681 2022/10/10 21:05:19 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:51:09 time: 0.374253 data_time: 0.066261 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.796337 loss: 0.000702 2022/10/10 21:05:37 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:50:53 time: 0.378472 data_time: 0.067333 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.805022 loss: 0.000699 2022/10/10 21:05:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:06:15 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:50:16 time: 0.384985 data_time: 0.086580 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.855821 loss: 0.000707 2022/10/10 21:06:34 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:50:01 time: 0.390544 data_time: 0.066754 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.822180 loss: 0.000690 2022/10/10 21:06:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:06:54 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:49:45 time: 0.398453 data_time: 0.074201 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.799765 loss: 0.000704 2022/10/10 21:07:14 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:49:30 time: 0.400381 data_time: 0.070405 memory: 6293 loss_kpt: 0.000694 acc_pose: 0.768321 loss: 0.000694 2022/10/10 21:07:34 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:49:14 time: 0.394057 data_time: 0.066917 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.797491 loss: 0.000688 2022/10/10 21:07:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:08:10 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:48:37 time: 0.391802 data_time: 0.094118 memory: 6293 loss_kpt: 0.000696 acc_pose: 0.814503 loss: 0.000696 2022/10/10 21:08:30 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:48:22 time: 0.391098 data_time: 0.065035 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.846654 loss: 0.000697 2022/10/10 21:08:49 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:48:06 time: 0.384154 data_time: 0.072409 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.824100 loss: 0.000699 2022/10/10 21:09:08 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:47:49 time: 0.366413 data_time: 0.072538 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.825500 loss: 0.000699 2022/10/10 21:09:28 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:47:34 time: 0.402522 data_time: 0.066896 memory: 6293 loss_kpt: 0.000711 acc_pose: 0.812384 loss: 0.000711 2022/10/10 21:09:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:10:05 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:46:58 time: 0.424199 data_time: 0.093624 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.843285 loss: 0.000700 2022/10/10 21:10:24 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:46:42 time: 0.370109 data_time: 0.070219 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.833205 loss: 0.000701 2022/10/10 21:10:44 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:46:26 time: 0.402142 data_time: 0.074394 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.814393 loss: 0.000707 2022/10/10 21:11:04 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:46:11 time: 0.406146 data_time: 0.069128 memory: 6293 loss_kpt: 0.000710 acc_pose: 0.847599 loss: 0.000710 2022/10/10 21:11:23 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:45:55 time: 0.380919 data_time: 0.068782 memory: 6293 loss_kpt: 0.000707 acc_pose: 0.821518 loss: 0.000707 2022/10/10 21:11:39 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:12:00 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:45:19 time: 0.423894 data_time: 0.101182 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.866986 loss: 0.000700 2022/10/10 21:12:20 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:45:04 time: 0.401640 data_time: 0.085384 memory: 6293 loss_kpt: 0.000695 acc_pose: 0.793416 loss: 0.000695 2022/10/10 21:12:40 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:44:48 time: 0.391117 data_time: 0.074232 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.837352 loss: 0.000705 2022/10/10 21:13:00 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:44:32 time: 0.400488 data_time: 0.072842 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.783621 loss: 0.000703 2022/10/10 21:13:08 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:13:18 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:44:16 time: 0.372946 data_time: 0.067647 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.821405 loss: 0.000700 2022/10/10 21:13:35 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:13:57 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:43:40 time: 0.433512 data_time: 0.087771 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.798096 loss: 0.000692 2022/10/10 21:14:16 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:43:24 time: 0.377541 data_time: 0.083961 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.815205 loss: 0.000691 2022/10/10 21:14:37 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:43:09 time: 0.417351 data_time: 0.074027 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.786124 loss: 0.000697 2022/10/10 21:14:57 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:42:54 time: 0.412892 data_time: 0.076656 memory: 6293 loss_kpt: 0.000686 acc_pose: 0.807158 loss: 0.000686 2022/10/10 21:15:16 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:42:38 time: 0.382273 data_time: 0.067164 memory: 6293 loss_kpt: 0.000693 acc_pose: 0.802573 loss: 0.000693 2022/10/10 21:15:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:15:55 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:42:02 time: 0.432454 data_time: 0.082269 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.844812 loss: 0.000701 2022/10/10 21:16:16 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:41:47 time: 0.406895 data_time: 0.076496 memory: 6293 loss_kpt: 0.000689 acc_pose: 0.786353 loss: 0.000689 2022/10/10 21:16:35 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:41:31 time: 0.396437 data_time: 0.080337 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.834467 loss: 0.000681 2022/10/10 21:16:55 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:41:16 time: 0.400706 data_time: 0.079624 memory: 6293 loss_kpt: 0.000703 acc_pose: 0.796396 loss: 0.000703 2022/10/10 21:17:15 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:41:00 time: 0.381787 data_time: 0.071905 memory: 6293 loss_kpt: 0.000708 acc_pose: 0.783357 loss: 0.000708 2022/10/10 21:17:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:17:51 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:40:23 time: 0.391641 data_time: 0.081561 memory: 6293 loss_kpt: 0.000709 acc_pose: 0.814204 loss: 0.000709 2022/10/10 21:18:10 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:40:07 time: 0.376167 data_time: 0.077175 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.809898 loss: 0.000699 2022/10/10 21:18:30 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:39:52 time: 0.404498 data_time: 0.079346 memory: 6293 loss_kpt: 0.000687 acc_pose: 0.758517 loss: 0.000687 2022/10/10 21:18:49 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:39:36 time: 0.389421 data_time: 0.089437 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.828847 loss: 0.000699 2022/10/10 21:19:12 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:39:21 time: 0.453243 data_time: 0.073821 memory: 6293 loss_kpt: 0.000696 acc_pose: 0.819910 loss: 0.000696 2022/10/10 21:19:28 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:19:28 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/10 21:19:38 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:56 time: 0.157036 data_time: 0.091290 memory: 6293 2022/10/10 21:19:45 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:42 time: 0.139596 data_time: 0.077334 memory: 533 2022/10/10 21:19:51 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:28 time: 0.112102 data_time: 0.049974 memory: 533 2022/10/10 21:19:56 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:23 time: 0.111515 data_time: 0.049297 memory: 533 2022/10/10 21:20:02 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:18 time: 0.116026 data_time: 0.052303 memory: 533 2022/10/10 21:20:09 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:13 time: 0.128749 data_time: 0.065821 memory: 533 2022/10/10 21:20:15 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:06 time: 0.121631 data_time: 0.060240 memory: 533 2022/10/10 21:20:20 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.106159 data_time: 0.046093 memory: 533 2022/10/10 21:20:52 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 21:21:04 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.688279 coco/AP .5: 0.882876 coco/AP .75: 0.766043 coco/AP (M): 0.658203 coco/AP (L): 0.750276 coco/AR: 0.746662 coco/AR .5: 0.925850 coco/AR .75: 0.817695 coco/AR (M): 0.706310 coco/AR (L): 0.804682 2022/10/10 21:21:04 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_130.pth is removed 2022/10/10 21:21:06 - mmengine - INFO - The best checkpoint with 0.6883 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/10 21:21:27 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:21:27 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:38:46 time: 0.434212 data_time: 0.099778 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.781758 loss: 0.000698 2022/10/10 21:21:50 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:38:31 time: 0.453631 data_time: 0.072800 memory: 6293 loss_kpt: 0.000684 acc_pose: 0.815692 loss: 0.000684 2022/10/10 21:22:10 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:38:15 time: 0.394275 data_time: 0.072531 memory: 6293 loss_kpt: 0.000705 acc_pose: 0.792647 loss: 0.000705 2022/10/10 21:22:29 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:37:59 time: 0.390683 data_time: 0.071636 memory: 6293 loss_kpt: 0.000680 acc_pose: 0.830697 loss: 0.000680 2022/10/10 21:22:49 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:37:43 time: 0.388012 data_time: 0.078409 memory: 6293 loss_kpt: 0.000702 acc_pose: 0.821507 loss: 0.000702 2022/10/10 21:23:05 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:23:26 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:37:08 time: 0.406109 data_time: 0.085043 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.873951 loss: 0.000698 2022/10/10 21:23:46 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:36:52 time: 0.403916 data_time: 0.069504 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.846913 loss: 0.000688 2022/10/10 21:24:05 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:36:36 time: 0.378506 data_time: 0.067871 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.814853 loss: 0.000697 2022/10/10 21:24:25 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:36:20 time: 0.413549 data_time: 0.066550 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.828929 loss: 0.000699 2022/10/10 21:24:46 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:36:05 time: 0.415069 data_time: 0.064436 memory: 6293 loss_kpt: 0.000700 acc_pose: 0.821256 loss: 0.000700 2022/10/10 21:25:02 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:25:23 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:35:29 time: 0.410017 data_time: 0.102872 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.844455 loss: 0.000699 2022/10/10 21:25:43 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:35:14 time: 0.398027 data_time: 0.077297 memory: 6293 loss_kpt: 0.000689 acc_pose: 0.795852 loss: 0.000689 2022/10/10 21:26:02 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:34:57 time: 0.381954 data_time: 0.070088 memory: 6293 loss_kpt: 0.000678 acc_pose: 0.837453 loss: 0.000678 2022/10/10 21:26:21 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:34:41 time: 0.374397 data_time: 0.071002 memory: 6293 loss_kpt: 0.000704 acc_pose: 0.799037 loss: 0.000704 2022/10/10 21:26:40 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:34:25 time: 0.391442 data_time: 0.074982 memory: 6293 loss_kpt: 0.000693 acc_pose: 0.854193 loss: 0.000693 2022/10/10 21:26:57 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:27:16 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:33:49 time: 0.390913 data_time: 0.083377 memory: 6293 loss_kpt: 0.000693 acc_pose: 0.820555 loss: 0.000693 2022/10/10 21:27:37 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:33:34 time: 0.406497 data_time: 0.080680 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.824357 loss: 0.000690 2022/10/10 21:27:57 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:33:18 time: 0.400019 data_time: 0.094344 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.842894 loss: 0.000698 2022/10/10 21:28:05 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:28:17 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:33:02 time: 0.395371 data_time: 0.082262 memory: 6293 loss_kpt: 0.000701 acc_pose: 0.845525 loss: 0.000701 2022/10/10 21:28:36 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:32:46 time: 0.388592 data_time: 0.075496 memory: 6293 loss_kpt: 0.000686 acc_pose: 0.833234 loss: 0.000686 2022/10/10 21:28:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:29:15 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:32:11 time: 0.431477 data_time: 0.116113 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.822172 loss: 0.000691 2022/10/10 21:29:35 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:31:55 time: 0.390165 data_time: 0.090255 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.842161 loss: 0.000682 2022/10/10 21:29:56 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:31:40 time: 0.427636 data_time: 0.089932 memory: 6293 loss_kpt: 0.000678 acc_pose: 0.809598 loss: 0.000678 2022/10/10 21:30:16 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:31:24 time: 0.395912 data_time: 0.074746 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.790909 loss: 0.000692 2022/10/10 21:30:36 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:31:08 time: 0.402994 data_time: 0.071816 memory: 6293 loss_kpt: 0.000699 acc_pose: 0.819481 loss: 0.000699 2022/10/10 21:30:52 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:31:14 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:30:33 time: 0.432153 data_time: 0.099514 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.809321 loss: 0.000682 2022/10/10 21:31:34 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:30:17 time: 0.404999 data_time: 0.079936 memory: 6293 loss_kpt: 0.000694 acc_pose: 0.821748 loss: 0.000694 2022/10/10 21:31:55 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:30:02 time: 0.423487 data_time: 0.068279 memory: 6293 loss_kpt: 0.000686 acc_pose: 0.835507 loss: 0.000686 2022/10/10 21:32:15 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:29:46 time: 0.399209 data_time: 0.071638 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.852009 loss: 0.000692 2022/10/10 21:32:34 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:29:30 time: 0.371392 data_time: 0.065070 memory: 6293 loss_kpt: 0.000687 acc_pose: 0.811897 loss: 0.000687 2022/10/10 21:32:50 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:33:13 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:28:55 time: 0.452904 data_time: 0.126257 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.839249 loss: 0.000692 2022/10/10 21:33:34 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:28:40 time: 0.422840 data_time: 0.082118 memory: 6293 loss_kpt: 0.000679 acc_pose: 0.839644 loss: 0.000679 2022/10/10 21:33:54 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:28:24 time: 0.395824 data_time: 0.075283 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.812321 loss: 0.000691 2022/10/10 21:34:13 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:28:08 time: 0.391408 data_time: 0.070860 memory: 6293 loss_kpt: 0.000680 acc_pose: 0.824099 loss: 0.000680 2022/10/10 21:34:34 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:27:52 time: 0.412958 data_time: 0.079478 memory: 6293 loss_kpt: 0.000678 acc_pose: 0.825812 loss: 0.000678 2022/10/10 21:34:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:34:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:35:12 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:27:17 time: 0.419766 data_time: 0.087702 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.859193 loss: 0.000682 2022/10/10 21:35:31 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:27:01 time: 0.385138 data_time: 0.072407 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.803886 loss: 0.000688 2022/10/10 21:35:52 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:26:45 time: 0.407459 data_time: 0.082224 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.798196 loss: 0.000681 2022/10/10 21:36:12 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:26:29 time: 0.406605 data_time: 0.077747 memory: 6293 loss_kpt: 0.000680 acc_pose: 0.815199 loss: 0.000680 2022/10/10 21:36:32 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:26:14 time: 0.400790 data_time: 0.068403 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.821629 loss: 0.000681 2022/10/10 21:36:50 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:37:11 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:25:39 time: 0.426446 data_time: 0.105447 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.833554 loss: 0.000682 2022/10/10 21:37:31 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:25:23 time: 0.388494 data_time: 0.075354 memory: 6293 loss_kpt: 0.000687 acc_pose: 0.863720 loss: 0.000687 2022/10/10 21:37:52 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:25:07 time: 0.416246 data_time: 0.087507 memory: 6293 loss_kpt: 0.000683 acc_pose: 0.834421 loss: 0.000683 2022/10/10 21:38:12 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:24:51 time: 0.411714 data_time: 0.093951 memory: 6293 loss_kpt: 0.000679 acc_pose: 0.850631 loss: 0.000679 2022/10/10 21:38:32 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:24:35 time: 0.402157 data_time: 0.100568 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.816529 loss: 0.000691 2022/10/10 21:38:49 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:39:08 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:24:00 time: 0.394498 data_time: 0.092895 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.812297 loss: 0.000681 2022/10/10 21:39:28 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:23:44 time: 0.384469 data_time: 0.074227 memory: 6293 loss_kpt: 0.000684 acc_pose: 0.854093 loss: 0.000684 2022/10/10 21:39:46 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:23:28 time: 0.374699 data_time: 0.070932 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.834845 loss: 0.000690 2022/10/10 21:40:07 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:23:12 time: 0.404681 data_time: 0.070508 memory: 6293 loss_kpt: 0.000694 acc_pose: 0.814375 loss: 0.000694 2022/10/10 21:40:26 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:22:56 time: 0.397894 data_time: 0.079677 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.801009 loss: 0.000691 2022/10/10 21:40:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:40:44 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/10 21:40:52 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:41 time: 0.116379 data_time: 0.051777 memory: 6293 2022/10/10 21:40:57 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:35 time: 0.116817 data_time: 0.053997 memory: 533 2022/10/10 21:41:03 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:30 time: 0.117986 data_time: 0.055734 memory: 533 2022/10/10 21:41:10 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:25 time: 0.125048 data_time: 0.062977 memory: 533 2022/10/10 21:41:16 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:19 time: 0.122555 data_time: 0.055413 memory: 533 2022/10/10 21:41:23 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:14 time: 0.136711 data_time: 0.074138 memory: 533 2022/10/10 21:41:29 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:07 time: 0.126688 data_time: 0.065081 memory: 533 2022/10/10 21:41:34 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.106383 data_time: 0.045658 memory: 533 2022/10/10 21:42:04 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 21:42:16 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.686799 coco/AP .5: 0.883804 coco/AP .75: 0.764906 coco/AP (M): 0.656105 coco/AP (L): 0.749632 coco/AR: 0.745403 coco/AR .5: 0.925220 coco/AR .75: 0.815806 coco/AR (M): 0.704944 coco/AR (L): 0.803939 2022/10/10 21:42:38 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:22:21 time: 0.438726 data_time: 0.093748 memory: 6293 loss_kpt: 0.000685 acc_pose: 0.795977 loss: 0.000685 2022/10/10 21:42:58 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:22:05 time: 0.389069 data_time: 0.096160 memory: 6293 loss_kpt: 0.000662 acc_pose: 0.834833 loss: 0.000662 2022/10/10 21:43:05 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:43:18 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:21:49 time: 0.395961 data_time: 0.069720 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.826354 loss: 0.000688 2022/10/10 21:43:36 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:21:33 time: 0.373113 data_time: 0.075859 memory: 6293 loss_kpt: 0.000698 acc_pose: 0.875705 loss: 0.000698 2022/10/10 21:43:56 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:21:17 time: 0.398939 data_time: 0.070237 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.847029 loss: 0.000692 2022/10/10 21:44:12 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:44:32 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:20:42 time: 0.404147 data_time: 0.080162 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.862882 loss: 0.000692 2022/10/10 21:44:52 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:20:26 time: 0.394581 data_time: 0.071513 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.841698 loss: 0.000682 2022/10/10 21:45:15 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:20:10 time: 0.452247 data_time: 0.081223 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.802368 loss: 0.000682 2022/10/10 21:45:33 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:19:54 time: 0.368466 data_time: 0.071185 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.875265 loss: 0.000691 2022/10/10 21:45:52 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:19:37 time: 0.373895 data_time: 0.074388 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.845411 loss: 0.000690 2022/10/10 21:46:07 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:46:29 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:19:03 time: 0.426626 data_time: 0.091186 memory: 6293 loss_kpt: 0.000683 acc_pose: 0.839324 loss: 0.000683 2022/10/10 21:46:49 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:18:47 time: 0.409756 data_time: 0.067519 memory: 6293 loss_kpt: 0.000677 acc_pose: 0.862547 loss: 0.000677 2022/10/10 21:47:07 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:18:31 time: 0.365180 data_time: 0.073117 memory: 6293 loss_kpt: 0.000689 acc_pose: 0.838793 loss: 0.000689 2022/10/10 21:47:28 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:18:15 time: 0.416940 data_time: 0.069350 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.827719 loss: 0.000688 2022/10/10 21:47:48 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:17:59 time: 0.392447 data_time: 0.065499 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.830182 loss: 0.000688 2022/10/10 21:48:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:48:25 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:17:24 time: 0.420010 data_time: 0.089085 memory: 6293 loss_kpt: 0.000686 acc_pose: 0.833893 loss: 0.000686 2022/10/10 21:48:44 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:17:08 time: 0.377488 data_time: 0.073685 memory: 6293 loss_kpt: 0.000674 acc_pose: 0.820343 loss: 0.000674 2022/10/10 21:49:03 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:16:52 time: 0.395226 data_time: 0.070225 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.764037 loss: 0.000688 2022/10/10 21:49:22 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:16:35 time: 0.371648 data_time: 0.072959 memory: 6293 loss_kpt: 0.000684 acc_pose: 0.807433 loss: 0.000684 2022/10/10 21:49:38 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:49:42 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:16:19 time: 0.390673 data_time: 0.079354 memory: 6293 loss_kpt: 0.000691 acc_pose: 0.787025 loss: 0.000691 2022/10/10 21:49:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:50:20 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:15:44 time: 0.403757 data_time: 0.084073 memory: 6293 loss_kpt: 0.000680 acc_pose: 0.798674 loss: 0.000680 2022/10/10 21:50:40 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:15:28 time: 0.405209 data_time: 0.074064 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.766829 loss: 0.000690 2022/10/10 21:50:59 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:15:12 time: 0.386887 data_time: 0.078076 memory: 6293 loss_kpt: 0.000697 acc_pose: 0.786456 loss: 0.000697 2022/10/10 21:51:18 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:14:56 time: 0.383315 data_time: 0.065686 memory: 6293 loss_kpt: 0.000679 acc_pose: 0.877161 loss: 0.000679 2022/10/10 21:51:37 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:14:39 time: 0.377825 data_time: 0.094213 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.821548 loss: 0.000688 2022/10/10 21:51:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:52:16 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:14:05 time: 0.434066 data_time: 0.091155 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.838254 loss: 0.000688 2022/10/10 21:52:36 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:13:49 time: 0.409681 data_time: 0.078984 memory: 6293 loss_kpt: 0.000684 acc_pose: 0.878071 loss: 0.000684 2022/10/10 21:52:56 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:13:33 time: 0.404571 data_time: 0.070455 memory: 6293 loss_kpt: 0.000684 acc_pose: 0.803121 loss: 0.000684 2022/10/10 21:53:17 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:13:17 time: 0.401171 data_time: 0.074705 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.827955 loss: 0.000688 2022/10/10 21:53:37 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:13:01 time: 0.407203 data_time: 0.102668 memory: 6293 loss_kpt: 0.000688 acc_pose: 0.859648 loss: 0.000688 2022/10/10 21:53:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:54:16 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:12:27 time: 0.436846 data_time: 0.087766 memory: 6293 loss_kpt: 0.000696 acc_pose: 0.838374 loss: 0.000696 2022/10/10 21:54:36 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:12:11 time: 0.399494 data_time: 0.072853 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.867733 loss: 0.000681 2022/10/10 21:54:57 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:11:55 time: 0.400947 data_time: 0.098545 memory: 6293 loss_kpt: 0.000675 acc_pose: 0.763366 loss: 0.000675 2022/10/10 21:55:17 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:11:39 time: 0.399610 data_time: 0.079637 memory: 6293 loss_kpt: 0.000684 acc_pose: 0.753918 loss: 0.000684 2022/10/10 21:55:36 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:11:22 time: 0.391929 data_time: 0.080144 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.818191 loss: 0.000690 2022/10/10 21:55:52 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:56:13 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:10:48 time: 0.413622 data_time: 0.082191 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.854695 loss: 0.000690 2022/10/10 21:56:20 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:56:32 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:10:32 time: 0.368834 data_time: 0.071568 memory: 6293 loss_kpt: 0.000672 acc_pose: 0.819657 loss: 0.000672 2022/10/10 21:56:50 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:10:15 time: 0.373547 data_time: 0.068357 memory: 6293 loss_kpt: 0.000690 acc_pose: 0.858659 loss: 0.000690 2022/10/10 21:57:09 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:09:59 time: 0.375571 data_time: 0.076764 memory: 6293 loss_kpt: 0.000681 acc_pose: 0.797962 loss: 0.000681 2022/10/10 21:57:29 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:09:42 time: 0.400916 data_time: 0.065257 memory: 6293 loss_kpt: 0.000686 acc_pose: 0.847029 loss: 0.000686 2022/10/10 21:57:45 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 21:58:05 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:09:08 time: 0.404666 data_time: 0.089875 memory: 6293 loss_kpt: 0.000693 acc_pose: 0.872215 loss: 0.000693 2022/10/10 21:58:26 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:08:52 time: 0.413378 data_time: 0.071362 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.833452 loss: 0.000682 2022/10/10 21:58:44 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:08:36 time: 0.377427 data_time: 0.077390 memory: 6293 loss_kpt: 0.000679 acc_pose: 0.785655 loss: 0.000679 2022/10/10 21:59:04 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:08:19 time: 0.388252 data_time: 0.070594 memory: 6293 loss_kpt: 0.000692 acc_pose: 0.871601 loss: 0.000692 2022/10/10 21:59:25 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:08:03 time: 0.422030 data_time: 0.082107 memory: 6293 loss_kpt: 0.000677 acc_pose: 0.830778 loss: 0.000677 2022/10/10 21:59:41 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:00:01 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:07:29 time: 0.397229 data_time: 0.088138 memory: 6293 loss_kpt: 0.000686 acc_pose: 0.818027 loss: 0.000686 2022/10/10 22:00:20 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:07:13 time: 0.376459 data_time: 0.080603 memory: 6293 loss_kpt: 0.000677 acc_pose: 0.827323 loss: 0.000677 2022/10/10 22:00:38 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:06:56 time: 0.366958 data_time: 0.067962 memory: 6293 loss_kpt: 0.000683 acc_pose: 0.829223 loss: 0.000683 2022/10/10 22:00:58 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:06:40 time: 0.398293 data_time: 0.081870 memory: 6293 loss_kpt: 0.000683 acc_pose: 0.800430 loss: 0.000683 2022/10/10 22:01:16 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:06:23 time: 0.363298 data_time: 0.081679 memory: 6293 loss_kpt: 0.000676 acc_pose: 0.811014 loss: 0.000676 2022/10/10 22:01:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:01:31 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/10 22:01:40 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:41 time: 0.116129 data_time: 0.051892 memory: 6293 2022/10/10 22:01:45 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:35 time: 0.115602 data_time: 0.054195 memory: 533 2022/10/10 22:01:52 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:33 time: 0.129105 data_time: 0.067010 memory: 533 2022/10/10 22:01:59 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:27 time: 0.132554 data_time: 0.068121 memory: 533 2022/10/10 22:02:05 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:19 time: 0.126441 data_time: 0.064123 memory: 533 2022/10/10 22:02:11 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:12 time: 0.114631 data_time: 0.052952 memory: 533 2022/10/10 22:02:17 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:06 time: 0.119756 data_time: 0.057090 memory: 533 2022/10/10 22:02:23 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.119994 data_time: 0.059122 memory: 533 2022/10/10 22:02:53 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 22:03:05 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.687612 coco/AP .5: 0.881128 coco/AP .75: 0.768330 coco/AP (M): 0.653656 coco/AP (L): 0.752917 coco/AR: 0.744805 coco/AR .5: 0.920970 coco/AR .75: 0.818955 coco/AR (M): 0.703114 coco/AR (L): 0.804942 2022/10/10 22:03:24 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:05:49 time: 0.376711 data_time: 0.080383 memory: 6293 loss_kpt: 0.000669 acc_pose: 0.842526 loss: 0.000669 2022/10/10 22:03:44 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:05:32 time: 0.392358 data_time: 0.072123 memory: 6293 loss_kpt: 0.000682 acc_pose: 0.783815 loss: 0.000682 2022/10/10 22:04:03 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 1:05:16 time: 0.389357 data_time: 0.075189 memory: 6293 loss_kpt: 0.000660 acc_pose: 0.834769 loss: 0.000660 2022/10/10 22:04:19 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:04:22 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 1:05:00 time: 0.386763 data_time: 0.072208 memory: 6293 loss_kpt: 0.000670 acc_pose: 0.820395 loss: 0.000670 2022/10/10 22:04:43 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 1:04:44 time: 0.417736 data_time: 0.075245 memory: 6293 loss_kpt: 0.000653 acc_pose: 0.864472 loss: 0.000653 2022/10/10 22:05:02 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:05:24 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 1:04:10 time: 0.449799 data_time: 0.080511 memory: 6293 loss_kpt: 0.000649 acc_pose: 0.897443 loss: 0.000649 2022/10/10 22:05:44 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 1:03:54 time: 0.394454 data_time: 0.098807 memory: 6293 loss_kpt: 0.000651 acc_pose: 0.877290 loss: 0.000651 2022/10/10 22:06:04 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 1:03:38 time: 0.394307 data_time: 0.086235 memory: 6293 loss_kpt: 0.000662 acc_pose: 0.853442 loss: 0.000662 2022/10/10 22:06:24 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 1:03:22 time: 0.415577 data_time: 0.075182 memory: 6293 loss_kpt: 0.000671 acc_pose: 0.823871 loss: 0.000671 2022/10/10 22:06:43 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 1:03:05 time: 0.365346 data_time: 0.080951 memory: 6293 loss_kpt: 0.000652 acc_pose: 0.837231 loss: 0.000652 2022/10/10 22:07:01 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:07:22 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 1:02:31 time: 0.417798 data_time: 0.082039 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.848728 loss: 0.000645 2022/10/10 22:07:41 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 1:02:15 time: 0.382232 data_time: 0.069397 memory: 6293 loss_kpt: 0.000660 acc_pose: 0.829789 loss: 0.000660 2022/10/10 22:08:00 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 1:01:58 time: 0.380310 data_time: 0.067035 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.827273 loss: 0.000638 2022/10/10 22:08:20 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 1:01:42 time: 0.392340 data_time: 0.076750 memory: 6293 loss_kpt: 0.000651 acc_pose: 0.803890 loss: 0.000651 2022/10/10 22:08:39 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 1:01:25 time: 0.391670 data_time: 0.069268 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.859969 loss: 0.000640 2022/10/10 22:08:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:09:17 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 1:00:52 time: 0.427623 data_time: 0.088929 memory: 6293 loss_kpt: 0.000647 acc_pose: 0.855997 loss: 0.000647 2022/10/10 22:09:37 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 1:00:36 time: 0.408597 data_time: 0.077808 memory: 6293 loss_kpt: 0.000654 acc_pose: 0.866491 loss: 0.000654 2022/10/10 22:09:57 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 1:00:19 time: 0.380400 data_time: 0.075463 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.821962 loss: 0.000641 2022/10/10 22:10:17 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 1:00:03 time: 0.410264 data_time: 0.071426 memory: 6293 loss_kpt: 0.000650 acc_pose: 0.802875 loss: 0.000650 2022/10/10 22:10:37 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:59:47 time: 0.404298 data_time: 0.071451 memory: 6293 loss_kpt: 0.000656 acc_pose: 0.834496 loss: 0.000656 2022/10/10 22:10:55 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:11:03 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:11:15 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:59:13 time: 0.406811 data_time: 0.091184 memory: 6293 loss_kpt: 0.000647 acc_pose: 0.848265 loss: 0.000647 2022/10/10 22:11:36 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:58:57 time: 0.405578 data_time: 0.090090 memory: 6293 loss_kpt: 0.000659 acc_pose: 0.839193 loss: 0.000659 2022/10/10 22:11:56 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:58:40 time: 0.400084 data_time: 0.071728 memory: 6293 loss_kpt: 0.000652 acc_pose: 0.863887 loss: 0.000652 2022/10/10 22:12:17 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:58:24 time: 0.419142 data_time: 0.089880 memory: 6293 loss_kpt: 0.000650 acc_pose: 0.888213 loss: 0.000650 2022/10/10 22:12:36 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:58:08 time: 0.380631 data_time: 0.063006 memory: 6293 loss_kpt: 0.000653 acc_pose: 0.852761 loss: 0.000653 2022/10/10 22:12:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:13:16 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:57:34 time: 0.439156 data_time: 0.088152 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.827597 loss: 0.000642 2022/10/10 22:13:36 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:57:18 time: 0.405443 data_time: 0.083799 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.811066 loss: 0.000641 2022/10/10 22:13:55 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:57:01 time: 0.368394 data_time: 0.074633 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.876255 loss: 0.000644 2022/10/10 22:14:14 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:56:45 time: 0.383412 data_time: 0.068768 memory: 6293 loss_kpt: 0.000656 acc_pose: 0.855430 loss: 0.000656 2022/10/10 22:14:33 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:56:28 time: 0.376762 data_time: 0.080292 memory: 6293 loss_kpt: 0.000630 acc_pose: 0.827169 loss: 0.000630 2022/10/10 22:14:50 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:15:10 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:55:55 time: 0.393526 data_time: 0.095824 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.831749 loss: 0.000638 2022/10/10 22:15:31 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:55:38 time: 0.411960 data_time: 0.101155 memory: 6293 loss_kpt: 0.000652 acc_pose: 0.902097 loss: 0.000652 2022/10/10 22:15:51 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:55:22 time: 0.397008 data_time: 0.079761 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.839929 loss: 0.000642 2022/10/10 22:16:11 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:55:06 time: 0.407291 data_time: 0.097877 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.868199 loss: 0.000641 2022/10/10 22:16:33 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:54:50 time: 0.433208 data_time: 0.075702 memory: 6293 loss_kpt: 0.000650 acc_pose: 0.852840 loss: 0.000650 2022/10/10 22:16:49 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:17:09 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:54:16 time: 0.394923 data_time: 0.093069 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.794308 loss: 0.000643 2022/10/10 22:17:29 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:54:00 time: 0.394810 data_time: 0.071869 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.858150 loss: 0.000638 2022/10/10 22:17:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:17:48 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:53:43 time: 0.384975 data_time: 0.069333 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.839300 loss: 0.000634 2022/10/10 22:18:11 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:53:27 time: 0.455788 data_time: 0.076124 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.839693 loss: 0.000634 2022/10/10 22:18:31 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:53:11 time: 0.397523 data_time: 0.071317 memory: 6293 loss_kpt: 0.000649 acc_pose: 0.815805 loss: 0.000649 2022/10/10 22:18:47 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:19:08 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:52:38 time: 0.426563 data_time: 0.093995 memory: 6293 loss_kpt: 0.000636 acc_pose: 0.850160 loss: 0.000636 2022/10/10 22:19:28 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:52:21 time: 0.397935 data_time: 0.098722 memory: 6293 loss_kpt: 0.000655 acc_pose: 0.825051 loss: 0.000655 2022/10/10 22:19:47 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:52:05 time: 0.388134 data_time: 0.077127 memory: 6293 loss_kpt: 0.000649 acc_pose: 0.819735 loss: 0.000649 2022/10/10 22:20:07 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:51:48 time: 0.384580 data_time: 0.068818 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.846723 loss: 0.000643 2022/10/10 22:20:25 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:51:31 time: 0.373240 data_time: 0.074632 memory: 6293 loss_kpt: 0.000646 acc_pose: 0.848447 loss: 0.000646 2022/10/10 22:20:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:21:05 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:50:58 time: 0.446534 data_time: 0.087576 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.884540 loss: 0.000644 2022/10/10 22:21:24 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:50:42 time: 0.375107 data_time: 0.076945 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.826896 loss: 0.000643 2022/10/10 22:21:43 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:50:25 time: 0.381494 data_time: 0.074104 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.840089 loss: 0.000642 2022/10/10 22:22:04 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:50:09 time: 0.410089 data_time: 0.094078 memory: 6293 loss_kpt: 0.000637 acc_pose: 0.841362 loss: 0.000637 2022/10/10 22:22:24 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:49:52 time: 0.398036 data_time: 0.073873 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.834982 loss: 0.000644 2022/10/10 22:22:41 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:22:41 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/10 22:22:49 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:42 time: 0.119133 data_time: 0.055920 memory: 6293 2022/10/10 22:22:56 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:39 time: 0.128478 data_time: 0.066001 memory: 533 2022/10/10 22:23:02 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:32 time: 0.124779 data_time: 0.062667 memory: 533 2022/10/10 22:23:08 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:24 time: 0.117432 data_time: 0.054214 memory: 533 2022/10/10 22:23:14 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:20 time: 0.129462 data_time: 0.066605 memory: 533 2022/10/10 22:23:20 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:11 time: 0.108047 data_time: 0.045927 memory: 533 2022/10/10 22:23:26 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:06 time: 0.116360 data_time: 0.053615 memory: 533 2022/10/10 22:23:31 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.107692 data_time: 0.046509 memory: 533 2022/10/10 22:24:01 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 22:24:13 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.697501 coco/AP .5: 0.886123 coco/AP .75: 0.781123 coco/AP (M): 0.666518 coco/AP (L): 0.761184 coco/AR: 0.755699 coco/AR .5: 0.928841 coco/AR .75: 0.831234 coco/AR (M): 0.715132 coco/AR (L): 0.814233 2022/10/10 22:24:13 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_150.pth is removed 2022/10/10 22:24:14 - mmengine - INFO - The best checkpoint with 0.6975 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/10 22:24:35 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:49:19 time: 0.422252 data_time: 0.086797 memory: 6293 loss_kpt: 0.000639 acc_pose: 0.871574 loss: 0.000639 2022/10/10 22:24:55 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:49:03 time: 0.385982 data_time: 0.079708 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.874360 loss: 0.000640 2022/10/10 22:25:16 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:48:46 time: 0.416840 data_time: 0.076236 memory: 6293 loss_kpt: 0.000651 acc_pose: 0.823628 loss: 0.000651 2022/10/10 22:25:35 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:48:30 time: 0.388983 data_time: 0.091990 memory: 6293 loss_kpt: 0.000650 acc_pose: 0.822698 loss: 0.000650 2022/10/10 22:25:54 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:48:13 time: 0.378384 data_time: 0.074748 memory: 6293 loss_kpt: 0.000647 acc_pose: 0.809508 loss: 0.000647 2022/10/10 22:25:58 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:26:11 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:26:33 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:47:40 time: 0.425830 data_time: 0.107746 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.781713 loss: 0.000641 2022/10/10 22:26:53 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:47:24 time: 0.405293 data_time: 0.067775 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.864983 loss: 0.000638 2022/10/10 22:27:13 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:47:07 time: 0.402745 data_time: 0.070444 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.831640 loss: 0.000641 2022/10/10 22:27:33 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:46:51 time: 0.394754 data_time: 0.073491 memory: 6293 loss_kpt: 0.000654 acc_pose: 0.859494 loss: 0.000654 2022/10/10 22:27:54 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:46:34 time: 0.424189 data_time: 0.081314 memory: 6293 loss_kpt: 0.000635 acc_pose: 0.874260 loss: 0.000635 2022/10/10 22:28:11 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:28:31 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:46:01 time: 0.392421 data_time: 0.086693 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.853645 loss: 0.000641 2022/10/10 22:28:50 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:45:44 time: 0.381231 data_time: 0.098705 memory: 6293 loss_kpt: 0.000650 acc_pose: 0.856054 loss: 0.000650 2022/10/10 22:29:09 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:45:28 time: 0.381560 data_time: 0.072575 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.825085 loss: 0.000643 2022/10/10 22:29:29 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:45:11 time: 0.412305 data_time: 0.088073 memory: 6293 loss_kpt: 0.000666 acc_pose: 0.800484 loss: 0.000666 2022/10/10 22:29:49 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:44:55 time: 0.382548 data_time: 0.080378 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.796879 loss: 0.000638 2022/10/10 22:30:05 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:30:26 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:44:22 time: 0.426685 data_time: 0.092007 memory: 6293 loss_kpt: 0.000630 acc_pose: 0.814155 loss: 0.000630 2022/10/10 22:30:45 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:44:05 time: 0.380858 data_time: 0.067110 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.862156 loss: 0.000645 2022/10/10 22:31:04 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:43:48 time: 0.371174 data_time: 0.072018 memory: 6293 loss_kpt: 0.000631 acc_pose: 0.843051 loss: 0.000631 2022/10/10 22:31:25 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:43:32 time: 0.419794 data_time: 0.072218 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.835871 loss: 0.000640 2022/10/10 22:31:44 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:43:15 time: 0.380348 data_time: 0.075308 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.838138 loss: 0.000642 2022/10/10 22:32:01 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:32:21 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:42:42 time: 0.401368 data_time: 0.078966 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.857701 loss: 0.000642 2022/10/10 22:32:36 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:32:40 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:42:26 time: 0.397340 data_time: 0.074762 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.803512 loss: 0.000644 2022/10/10 22:32:58 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:42:09 time: 0.350331 data_time: 0.069181 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.854969 loss: 0.000645 2022/10/10 22:33:18 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:41:52 time: 0.394551 data_time: 0.073584 memory: 6293 loss_kpt: 0.000637 acc_pose: 0.864229 loss: 0.000637 2022/10/10 22:33:38 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:41:36 time: 0.409588 data_time: 0.079064 memory: 6293 loss_kpt: 0.000635 acc_pose: 0.840966 loss: 0.000635 2022/10/10 22:33:54 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:34:13 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:41:03 time: 0.393003 data_time: 0.092975 memory: 6293 loss_kpt: 0.000639 acc_pose: 0.819725 loss: 0.000639 2022/10/10 22:34:33 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:40:46 time: 0.392316 data_time: 0.082548 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.847468 loss: 0.000634 2022/10/10 22:34:54 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:40:30 time: 0.411678 data_time: 0.084744 memory: 6293 loss_kpt: 0.000623 acc_pose: 0.818178 loss: 0.000623 2022/10/10 22:35:13 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:40:13 time: 0.389351 data_time: 0.073443 memory: 6293 loss_kpt: 0.000658 acc_pose: 0.837250 loss: 0.000658 2022/10/10 22:35:32 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:39:57 time: 0.379360 data_time: 0.072756 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.849346 loss: 0.000638 2022/10/10 22:35:48 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:36:09 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:39:24 time: 0.421587 data_time: 0.106656 memory: 6293 loss_kpt: 0.000639 acc_pose: 0.787504 loss: 0.000639 2022/10/10 22:36:29 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:39:07 time: 0.390042 data_time: 0.074855 memory: 6293 loss_kpt: 0.000627 acc_pose: 0.852349 loss: 0.000627 2022/10/10 22:36:49 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:38:51 time: 0.399097 data_time: 0.084689 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.815971 loss: 0.000645 2022/10/10 22:37:08 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:38:34 time: 0.391787 data_time: 0.065633 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.824219 loss: 0.000643 2022/10/10 22:37:28 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:38:17 time: 0.382978 data_time: 0.073389 memory: 6293 loss_kpt: 0.000650 acc_pose: 0.822281 loss: 0.000650 2022/10/10 22:37:44 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:38:05 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:37:45 time: 0.422465 data_time: 0.087952 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.861813 loss: 0.000634 2022/10/10 22:38:24 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:37:28 time: 0.392811 data_time: 0.064685 memory: 6293 loss_kpt: 0.000628 acc_pose: 0.895325 loss: 0.000628 2022/10/10 22:38:45 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:37:11 time: 0.414682 data_time: 0.068327 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.867358 loss: 0.000634 2022/10/10 22:39:04 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:36:55 time: 0.387291 data_time: 0.073165 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.868104 loss: 0.000638 2022/10/10 22:39:09 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:39:26 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:36:38 time: 0.423258 data_time: 0.093684 memory: 6293 loss_kpt: 0.000656 acc_pose: 0.831873 loss: 0.000656 2022/10/10 22:39:43 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:40:03 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:36:06 time: 0.413129 data_time: 0.089514 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.817687 loss: 0.000634 2022/10/10 22:40:24 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:35:49 time: 0.408584 data_time: 0.075172 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.849508 loss: 0.000641 2022/10/10 22:40:44 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:35:32 time: 0.402057 data_time: 0.071634 memory: 6293 loss_kpt: 0.000651 acc_pose: 0.842389 loss: 0.000651 2022/10/10 22:41:03 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:35:16 time: 0.384486 data_time: 0.078855 memory: 6293 loss_kpt: 0.000659 acc_pose: 0.828297 loss: 0.000659 2022/10/10 22:41:24 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:34:59 time: 0.414043 data_time: 0.083759 memory: 6293 loss_kpt: 0.000654 acc_pose: 0.812476 loss: 0.000654 2022/10/10 22:41:40 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:42:01 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:34:27 time: 0.409593 data_time: 0.103911 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.877371 loss: 0.000644 2022/10/10 22:42:19 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:34:10 time: 0.368300 data_time: 0.082290 memory: 6293 loss_kpt: 0.000635 acc_pose: 0.847162 loss: 0.000635 2022/10/10 22:42:38 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:33:53 time: 0.367302 data_time: 0.065653 memory: 6293 loss_kpt: 0.000647 acc_pose: 0.876392 loss: 0.000647 2022/10/10 22:42:56 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:33:36 time: 0.376142 data_time: 0.075421 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.852202 loss: 0.000640 2022/10/10 22:43:15 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:33:20 time: 0.382515 data_time: 0.064040 memory: 6293 loss_kpt: 0.000658 acc_pose: 0.793687 loss: 0.000658 2022/10/10 22:43:30 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:43:30 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/10 22:43:39 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:48 time: 0.137042 data_time: 0.072367 memory: 6293 2022/10/10 22:43:46 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:38 time: 0.125188 data_time: 0.062767 memory: 533 2022/10/10 22:43:52 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:32 time: 0.127026 data_time: 0.062727 memory: 533 2022/10/10 22:43:58 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:23 time: 0.114693 data_time: 0.051401 memory: 533 2022/10/10 22:44:04 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:19 time: 0.124400 data_time: 0.061561 memory: 533 2022/10/10 22:44:10 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:11 time: 0.110803 data_time: 0.047634 memory: 533 2022/10/10 22:44:15 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:06 time: 0.114672 data_time: 0.052161 memory: 533 2022/10/10 22:44:21 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.106718 data_time: 0.045365 memory: 533 2022/10/10 22:44:51 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 22:45:03 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.699016 coco/AP .5: 0.886831 coco/AP .75: 0.779689 coco/AP (M): 0.668736 coco/AP (L): 0.762436 coco/AR: 0.757021 coco/AR .5: 0.929471 coco/AR .75: 0.830605 coco/AR (M): 0.717673 coco/AR (L): 0.813861 2022/10/10 22:45:03 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_180.pth is removed 2022/10/10 22:45:05 - mmengine - INFO - The best checkpoint with 0.6990 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/10 22:45:25 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:32:47 time: 0.406362 data_time: 0.096646 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.885028 loss: 0.000638 2022/10/10 22:45:45 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:32:30 time: 0.394526 data_time: 0.087315 memory: 6293 loss_kpt: 0.000627 acc_pose: 0.855512 loss: 0.000627 2022/10/10 22:46:06 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:32:14 time: 0.429378 data_time: 0.083671 memory: 6293 loss_kpt: 0.000648 acc_pose: 0.859136 loss: 0.000648 2022/10/10 22:46:27 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:31:57 time: 0.411672 data_time: 0.071906 memory: 6293 loss_kpt: 0.000649 acc_pose: 0.823174 loss: 0.000649 2022/10/10 22:46:47 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:31:41 time: 0.403625 data_time: 0.091851 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.850840 loss: 0.000642 2022/10/10 22:47:03 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:47:19 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:47:25 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:31:08 time: 0.424505 data_time: 0.108931 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.833162 loss: 0.000640 2022/10/10 22:47:44 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:30:51 time: 0.390995 data_time: 0.068770 memory: 6293 loss_kpt: 0.000652 acc_pose: 0.829084 loss: 0.000652 2022/10/10 22:48:05 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:30:35 time: 0.410026 data_time: 0.105794 memory: 6293 loss_kpt: 0.000626 acc_pose: 0.819203 loss: 0.000626 2022/10/10 22:48:26 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:30:18 time: 0.431860 data_time: 0.072263 memory: 6293 loss_kpt: 0.000649 acc_pose: 0.797336 loss: 0.000649 2022/10/10 22:48:46 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:30:02 time: 0.385881 data_time: 0.070586 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.852484 loss: 0.000634 2022/10/10 22:49:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:49:27 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:29:29 time: 0.459675 data_time: 0.094715 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.860168 loss: 0.000643 2022/10/10 22:49:47 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:29:13 time: 0.404067 data_time: 0.078323 memory: 6293 loss_kpt: 0.000633 acc_pose: 0.874594 loss: 0.000633 2022/10/10 22:50:06 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:28:56 time: 0.381466 data_time: 0.067892 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.859973 loss: 0.000644 2022/10/10 22:50:26 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:28:39 time: 0.394515 data_time: 0.071403 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.829711 loss: 0.000642 2022/10/10 22:50:48 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:28:23 time: 0.434199 data_time: 0.080549 memory: 6293 loss_kpt: 0.000631 acc_pose: 0.882209 loss: 0.000631 2022/10/10 22:51:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:51:24 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:27:50 time: 0.393804 data_time: 0.094653 memory: 6293 loss_kpt: 0.000636 acc_pose: 0.801222 loss: 0.000636 2022/10/10 22:51:42 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:27:33 time: 0.358856 data_time: 0.068794 memory: 6293 loss_kpt: 0.000628 acc_pose: 0.857612 loss: 0.000628 2022/10/10 22:52:02 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:27:17 time: 0.404605 data_time: 0.084213 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.844523 loss: 0.000644 2022/10/10 22:52:22 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:27:00 time: 0.397402 data_time: 0.071361 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.873958 loss: 0.000634 2022/10/10 22:52:41 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:26:43 time: 0.375891 data_time: 0.066732 memory: 6293 loss_kpt: 0.000630 acc_pose: 0.807654 loss: 0.000630 2022/10/10 22:52:58 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:53:20 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:26:11 time: 0.431911 data_time: 0.091874 memory: 6293 loss_kpt: 0.000649 acc_pose: 0.820310 loss: 0.000649 2022/10/10 22:53:39 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:25:54 time: 0.381344 data_time: 0.071064 memory: 6293 loss_kpt: 0.000648 acc_pose: 0.845691 loss: 0.000648 2022/10/10 22:53:59 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:25:37 time: 0.398625 data_time: 0.071478 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.886766 loss: 0.000634 2022/10/10 22:54:02 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:54:19 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:25:21 time: 0.403729 data_time: 0.072343 memory: 6293 loss_kpt: 0.000627 acc_pose: 0.877977 loss: 0.000627 2022/10/10 22:54:37 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:25:04 time: 0.366988 data_time: 0.069663 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.891396 loss: 0.000643 2022/10/10 22:54:53 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:55:14 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:24:31 time: 0.414046 data_time: 0.080402 memory: 6293 loss_kpt: 0.000620 acc_pose: 0.850800 loss: 0.000620 2022/10/10 22:55:33 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:24:15 time: 0.372350 data_time: 0.066692 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.875490 loss: 0.000640 2022/10/10 22:55:51 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:23:58 time: 0.374932 data_time: 0.074209 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.860439 loss: 0.000641 2022/10/10 22:56:12 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:23:41 time: 0.413586 data_time: 0.083205 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.851606 loss: 0.000640 2022/10/10 22:56:32 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:23:24 time: 0.392557 data_time: 0.070019 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.860491 loss: 0.000645 2022/10/10 22:56:48 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:57:08 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:22:52 time: 0.390618 data_time: 0.085451 memory: 6293 loss_kpt: 0.000631 acc_pose: 0.865592 loss: 0.000631 2022/10/10 22:57:26 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:22:35 time: 0.363549 data_time: 0.073022 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.865779 loss: 0.000641 2022/10/10 22:57:45 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:22:18 time: 0.371505 data_time: 0.075225 memory: 6293 loss_kpt: 0.000652 acc_pose: 0.800927 loss: 0.000652 2022/10/10 22:58:04 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:22:02 time: 0.392647 data_time: 0.069202 memory: 6293 loss_kpt: 0.000626 acc_pose: 0.861655 loss: 0.000626 2022/10/10 22:58:23 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:21:45 time: 0.376941 data_time: 0.085217 memory: 6293 loss_kpt: 0.000624 acc_pose: 0.879723 loss: 0.000624 2022/10/10 22:58:40 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 22:59:01 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:21:13 time: 0.421426 data_time: 0.088791 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.864628 loss: 0.000644 2022/10/10 22:59:19 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:20:56 time: 0.373119 data_time: 0.077500 memory: 6293 loss_kpt: 0.000635 acc_pose: 0.877059 loss: 0.000635 2022/10/10 22:59:39 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:20:39 time: 0.388051 data_time: 0.075970 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.855420 loss: 0.000641 2022/10/10 22:59:58 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:20:22 time: 0.374407 data_time: 0.074003 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.839335 loss: 0.000643 2022/10/10 23:00:17 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:20:05 time: 0.388368 data_time: 0.068049 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.834379 loss: 0.000641 2022/10/10 23:00:29 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:00:34 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:00:55 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:19:33 time: 0.422124 data_time: 0.092475 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.883837 loss: 0.000643 2022/10/10 23:01:16 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:19:16 time: 0.405599 data_time: 0.079076 memory: 6293 loss_kpt: 0.000632 acc_pose: 0.852288 loss: 0.000632 2022/10/10 23:01:34 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:19:00 time: 0.365338 data_time: 0.081650 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.874686 loss: 0.000640 2022/10/10 23:01:54 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:18:43 time: 0.398459 data_time: 0.088218 memory: 6293 loss_kpt: 0.000628 acc_pose: 0.839010 loss: 0.000628 2022/10/10 23:02:13 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:18:26 time: 0.389434 data_time: 0.063155 memory: 6293 loss_kpt: 0.000636 acc_pose: 0.867430 loss: 0.000636 2022/10/10 23:02:31 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:02:52 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:17:54 time: 0.415720 data_time: 0.102300 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.836120 loss: 0.000638 2022/10/10 23:03:12 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:17:37 time: 0.389369 data_time: 0.081110 memory: 6293 loss_kpt: 0.000628 acc_pose: 0.869670 loss: 0.000628 2022/10/10 23:03:31 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:17:20 time: 0.382085 data_time: 0.074631 memory: 6293 loss_kpt: 0.000623 acc_pose: 0.852502 loss: 0.000623 2022/10/10 23:03:50 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:17:04 time: 0.378126 data_time: 0.071721 memory: 6293 loss_kpt: 0.000628 acc_pose: 0.846438 loss: 0.000628 2022/10/10 23:04:08 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:16:47 time: 0.370881 data_time: 0.072670 memory: 6293 loss_kpt: 0.000631 acc_pose: 0.834038 loss: 0.000631 2022/10/10 23:04:26 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:04:26 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/10 23:04:35 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:44 time: 0.125258 data_time: 0.062482 memory: 6293 2022/10/10 23:04:41 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:37 time: 0.123326 data_time: 0.060835 memory: 533 2022/10/10 23:04:48 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:34 time: 0.135725 data_time: 0.072788 memory: 533 2022/10/10 23:04:54 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:26 time: 0.129870 data_time: 0.062871 memory: 533 2022/10/10 23:05:00 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:19 time: 0.124244 data_time: 0.061233 memory: 533 2022/10/10 23:05:06 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:13 time: 0.121752 data_time: 0.060068 memory: 533 2022/10/10 23:05:13 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:07 time: 0.128338 data_time: 0.067049 memory: 533 2022/10/10 23:05:18 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.106300 data_time: 0.045392 memory: 533 2022/10/10 23:05:48 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 23:06:00 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.700035 coco/AP .5: 0.886551 coco/AP .75: 0.782839 coco/AP (M): 0.668596 coco/AP (L): 0.763294 coco/AR: 0.757856 coco/AR .5: 0.928841 coco/AR .75: 0.833753 coco/AR (M): 0.717400 coco/AR (L): 0.815942 2022/10/10 23:06:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/202210010/vipnas_mbv3/best_coco/AP_epoch_190.pth is removed 2022/10/10 23:06:02 - mmengine - INFO - The best checkpoint with 0.7000 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/10 23:06:21 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:16:15 time: 0.375779 data_time: 0.090020 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.834551 loss: 0.000641 2022/10/10 23:06:40 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:15:58 time: 0.379260 data_time: 0.075978 memory: 6293 loss_kpt: 0.000639 acc_pose: 0.829745 loss: 0.000639 2022/10/10 23:06:59 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:15:41 time: 0.396402 data_time: 0.060614 memory: 6293 loss_kpt: 0.000631 acc_pose: 0.822484 loss: 0.000631 2022/10/10 23:07:18 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:15:24 time: 0.380598 data_time: 0.076173 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.820236 loss: 0.000642 2022/10/10 23:07:38 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:15:07 time: 0.383133 data_time: 0.073179 memory: 6293 loss_kpt: 0.000633 acc_pose: 0.857923 loss: 0.000633 2022/10/10 23:07:56 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:08:15 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:14:35 time: 0.386379 data_time: 0.091185 memory: 6293 loss_kpt: 0.000629 acc_pose: 0.849751 loss: 0.000629 2022/10/10 23:08:34 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:14:18 time: 0.380236 data_time: 0.068532 memory: 6293 loss_kpt: 0.000635 acc_pose: 0.792435 loss: 0.000635 2022/10/10 23:08:37 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:08:53 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:14:02 time: 0.384530 data_time: 0.070764 memory: 6293 loss_kpt: 0.000642 acc_pose: 0.868564 loss: 0.000642 2022/10/10 23:09:13 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:13:45 time: 0.395140 data_time: 0.068333 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.827464 loss: 0.000638 2022/10/10 23:09:32 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:13:28 time: 0.383811 data_time: 0.069896 memory: 6293 loss_kpt: 0.000633 acc_pose: 0.870927 loss: 0.000633 2022/10/10 23:09:48 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:10:09 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:12:56 time: 0.431011 data_time: 0.087734 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.845430 loss: 0.000640 2022/10/10 23:10:28 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:12:39 time: 0.363563 data_time: 0.077995 memory: 6293 loss_kpt: 0.000646 acc_pose: 0.850520 loss: 0.000646 2022/10/10 23:10:45 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:12:22 time: 0.357069 data_time: 0.080623 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.870169 loss: 0.000638 2022/10/10 23:11:04 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:12:05 time: 0.367203 data_time: 0.072308 memory: 6293 loss_kpt: 0.000639 acc_pose: 0.850763 loss: 0.000639 2022/10/10 23:11:21 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:11:48 time: 0.350422 data_time: 0.071296 memory: 6293 loss_kpt: 0.000622 acc_pose: 0.873575 loss: 0.000622 2022/10/10 23:11:37 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:11:55 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:11:17 time: 0.374015 data_time: 0.092677 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.857119 loss: 0.000641 2022/10/10 23:12:13 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:11:00 time: 0.349053 data_time: 0.071201 memory: 6293 loss_kpt: 0.000632 acc_pose: 0.898229 loss: 0.000632 2022/10/10 23:12:30 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:10:43 time: 0.349316 data_time: 0.072691 memory: 6293 loss_kpt: 0.000626 acc_pose: 0.855256 loss: 0.000626 2022/10/10 23:12:49 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:10:26 time: 0.364319 data_time: 0.080386 memory: 6293 loss_kpt: 0.000626 acc_pose: 0.832083 loss: 0.000626 2022/10/10 23:13:06 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:10:09 time: 0.351566 data_time: 0.076999 memory: 6293 loss_kpt: 0.000631 acc_pose: 0.812962 loss: 0.000631 2022/10/10 23:13:22 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:13:42 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:09:37 time: 0.407604 data_time: 0.102310 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.854523 loss: 0.000634 2022/10/10 23:14:00 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:09:20 time: 0.367370 data_time: 0.070149 memory: 6293 loss_kpt: 0.000637 acc_pose: 0.886875 loss: 0.000637 2022/10/10 23:14:18 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:09:03 time: 0.347807 data_time: 0.068568 memory: 6293 loss_kpt: 0.000635 acc_pose: 0.839729 loss: 0.000635 2022/10/10 23:14:37 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:08:47 time: 0.389713 data_time: 0.073370 memory: 6293 loss_kpt: 0.000621 acc_pose: 0.856300 loss: 0.000621 2022/10/10 23:14:49 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:14:58 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:08:30 time: 0.409070 data_time: 0.077745 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.812056 loss: 0.000645 2022/10/10 23:15:16 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:15:35 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:07:58 time: 0.389420 data_time: 0.090735 memory: 6293 loss_kpt: 0.000629 acc_pose: 0.885859 loss: 0.000629 2022/10/10 23:15:54 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:07:41 time: 0.383838 data_time: 0.065193 memory: 6293 loss_kpt: 0.000624 acc_pose: 0.865556 loss: 0.000624 2022/10/10 23:16:13 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:07:24 time: 0.380556 data_time: 0.073288 memory: 6293 loss_kpt: 0.000643 acc_pose: 0.865657 loss: 0.000643 2022/10/10 23:16:34 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:07:07 time: 0.412333 data_time: 0.069175 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.817186 loss: 0.000645 2022/10/10 23:16:53 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:06:51 time: 0.380473 data_time: 0.075270 memory: 6293 loss_kpt: 0.000624 acc_pose: 0.857063 loss: 0.000624 2022/10/10 23:17:10 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:17:30 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:06:19 time: 0.396351 data_time: 0.090864 memory: 6293 loss_kpt: 0.000641 acc_pose: 0.855736 loss: 0.000641 2022/10/10 23:17:48 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:06:02 time: 0.374429 data_time: 0.086619 memory: 6293 loss_kpt: 0.000644 acc_pose: 0.818637 loss: 0.000644 2022/10/10 23:18:06 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:05:45 time: 0.361250 data_time: 0.081078 memory: 6293 loss_kpt: 0.000636 acc_pose: 0.836983 loss: 0.000636 2022/10/10 23:18:27 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:05:28 time: 0.406858 data_time: 0.097934 memory: 6293 loss_kpt: 0.000625 acc_pose: 0.829006 loss: 0.000625 2022/10/10 23:18:46 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:05:11 time: 0.388659 data_time: 0.100533 memory: 6293 loss_kpt: 0.000634 acc_pose: 0.850754 loss: 0.000634 2022/10/10 23:19:04 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:19:24 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:04:40 time: 0.406357 data_time: 0.090775 memory: 6293 loss_kpt: 0.000639 acc_pose: 0.814531 loss: 0.000639 2022/10/10 23:19:45 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:04:23 time: 0.408547 data_time: 0.080350 memory: 6293 loss_kpt: 0.000632 acc_pose: 0.844707 loss: 0.000632 2022/10/10 23:20:04 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:04:06 time: 0.385720 data_time: 0.067790 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.814893 loss: 0.000645 2022/10/10 23:20:25 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:49 time: 0.422058 data_time: 0.073376 memory: 6293 loss_kpt: 0.000637 acc_pose: 0.886358 loss: 0.000637 2022/10/10 23:20:44 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:03:32 time: 0.376958 data_time: 0.068435 memory: 6293 loss_kpt: 0.000638 acc_pose: 0.867899 loss: 0.000638 2022/10/10 23:20:59 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:21:19 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:03:01 time: 0.398264 data_time: 0.076689 memory: 6293 loss_kpt: 0.000637 acc_pose: 0.857912 loss: 0.000637 2022/10/10 23:21:21 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:21:38 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:44 time: 0.384653 data_time: 0.099001 memory: 6293 loss_kpt: 0.000623 acc_pose: 0.863773 loss: 0.000623 2022/10/10 23:22:00 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:27 time: 0.440239 data_time: 0.100310 memory: 6293 loss_kpt: 0.000636 acc_pose: 0.852459 loss: 0.000636 2022/10/10 23:22:20 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:02:10 time: 0.392592 data_time: 0.069622 memory: 6293 loss_kpt: 0.000626 acc_pose: 0.846919 loss: 0.000626 2022/10/10 23:22:39 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:53 time: 0.387062 data_time: 0.071493 memory: 6293 loss_kpt: 0.000640 acc_pose: 0.837270 loss: 0.000640 2022/10/10 23:22:57 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:23:18 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:22 time: 0.417273 data_time: 0.075862 memory: 6293 loss_kpt: 0.000645 acc_pose: 0.803812 loss: 0.000645 2022/10/10 23:23:37 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:01:05 time: 0.378127 data_time: 0.074904 memory: 6293 loss_kpt: 0.000625 acc_pose: 0.877769 loss: 0.000625 2022/10/10 23:23:56 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:48 time: 0.382634 data_time: 0.070511 memory: 6293 loss_kpt: 0.000625 acc_pose: 0.846418 loss: 0.000625 2022/10/10 23:24:15 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:31 time: 0.373763 data_time: 0.078859 memory: 6293 loss_kpt: 0.000637 acc_pose: 0.880637 loss: 0.000637 2022/10/10 23:24:34 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:14 time: 0.373866 data_time: 0.066470 memory: 6293 loss_kpt: 0.000632 acc_pose: 0.861323 loss: 0.000632 2022/10/10 23:24:51 - mmengine - INFO - Exp name: td-hm_vipnas-mbv3_8xb64-210e_coco-256x192_20221010_160745 2022/10/10 23:24:51 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/10 23:24:59 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:41 time: 0.115803 data_time: 0.053993 memory: 6293 2022/10/10 23:25:05 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:39 time: 0.128038 data_time: 0.065286 memory: 533 2022/10/10 23:25:11 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:28 time: 0.111767 data_time: 0.049729 memory: 533 2022/10/10 23:25:17 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:24 time: 0.119189 data_time: 0.056539 memory: 533 2022/10/10 23:25:23 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:17 time: 0.114175 data_time: 0.050960 memory: 533 2022/10/10 23:25:28 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:12 time: 0.113743 data_time: 0.051742 memory: 533 2022/10/10 23:25:34 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.111365 data_time: 0.049550 memory: 533 2022/10/10 23:25:39 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.108579 data_time: 0.046338 memory: 533 2022/10/10 23:26:11 - mmengine - INFO - Evaluating CocoMetric... 2022/10/10 23:26:22 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.699562 coco/AP .5: 0.886227 coco/AP .75: 0.781139 coco/AP (M): 0.668103 coco/AP (L): 0.763161 coco/AR: 0.758155 coco/AR .5: 0.928526 coco/AR .75: 0.831549 coco/AR (M): 0.718001 coco/AR (L): 0.815756